DIST-FL: Enhancing Security for TEE-based Aggregation in Federated Learning | Paper Citation Record

Observation 3baedf60-c050-435e-b13c-c7276711bd6a · outbound

This paper cites Advances and open problems in federated learning,.

DIST-FL: Enhancing Security for TEE-based Aggregation in Federated Learning Advances and open problems in federated learning,

Reference 1

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Observation cf3165f6-a50a-46e8-b0f3-fdffb23673ac · outbound

This paper cites Federated Learning: Strategies for Improving Communication Efficiency.

DIST-FL: Enhancing Security for TEE-based Aggregation in Federated Learning Federated Learning: Strategies for Improving Communication Efficiency

Reference 2

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Observation 1db594ea-0984-40a2-8fce-a2fa452694b1 · outbound

This paper cites Communication-efficient learning of deep networks from decentralized data,.

DIST-FL: Enhancing Security for TEE-based Aggregation in Federated Learning Communication-efficient learning of deep networks from decentralized data,

Reference 3

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Observation d675226a-8f37-4495-b4ea-286bc2d58f1c · outbound

This paper cites General data protection regulation,.

DIST-FL: Enhancing Security for TEE-based Aggregation in Federated Learning General data protection regulation,

Reference 4

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This paper cites American data privacy and protection act,.

DIST-FL: Enhancing Security for TEE-based Aggregation in Federated Learning American data privacy and protection act,

Reference 5

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Observation 1d4be686-f6b4-4fba-9e41-ca8fb7cbbcba · outbound

This paper cites Collaborative machine learning without centralized train- ing data,.

DIST-FL: Enhancing Security for TEE-based Aggregation in Federated Learning Collaborative machine learning without centralized train- ing data,

Reference 6

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Observation 269ff109-0ef3-43f7-ac77-2dfe68a203a2 · outbound

This paper cites Federated evaluation and tuning for on-device personalization: System design & applications,.

DIST-FL: Enhancing Security for TEE-based Aggregation in Federated Learning Federated evaluation and tuning for on-device personalization: System design & applications,

Reference 7

Resolution: unresolved; no resolver link, observed 2026-06-28T05:46:08.181285Z

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Observation f2b03175-a4e1-44fc-9674-20f9417fce11 · outbound

This paper cites Utilization of fate in risk management of credit in small and micro enterprises,.

DIST-FL: Enhancing Security for TEE-based Aggregation in Federated Learning Utilization of fate in risk management of credit in small and micro enterprises,

Reference 8

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Observation 895d83bf-3de2-44e9-b315-dbb450153131 · outbound

This paper cites Secure single-server aggregation with (poly) logarithmic overhead,.

DIST-FL: Enhancing Security for TEE-based Aggregation in Federated Learning Secure single-server aggregation with (poly) logarithmic overhead,

Reference 9

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Observation b7f879cc-cbf6-4908-b43d-21a471ef5563 · outbound

This paper cites Ppfl: privacy-preserving federated learning with trusted execution environments,.

DIST-FL: Enhancing Security for TEE-based Aggregation in Federated Learning Ppfl: privacy-preserving federated learning with trusted execution environments,

Reference 10

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Observation 4382d34d-d954-406f-8d60-d69584d50d82 · outbound

This paper cites Microsoft azure confidential computing with intel sgx,.

DIST-FL: Enhancing Security for TEE-based Aggregation in Federated Learning Microsoft azure confidential computing with intel sgx,

Reference 11

Resolution: unresolved; no resolver link, observed 2026-06-28T05:46:08.181285Z

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Observation 77530665-8346-4565-b7a0-1c958b6477e9 · outbound

This paper cites Distributed learning in trusted execution environment: A case study of federated learning in sgx,.

DIST-FL: Enhancing Security for TEE-based Aggregation in Federated Learning Distributed learning in trusted execution environment: A case study of federated learning in sgx,

Reference 12

Resolution: unresolved; no resolver link, observed 2026-06-28T05:46:08.181285Z

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Observation 6f58074a-9422-4a23-901e-3f1ffcebe62a · outbound

This paper cites Efficient and private federated learning using tee,.

DIST-FL: Enhancing Security for TEE-based Aggregation in Federated Learning Efficient and private federated learning using tee,

Reference 13

Resolution: unresolved; no resolver link, observed 2026-06-28T05:46:08.181285Z

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Observation d6168802-4c7c-40ae-a0a7-6d37ee8a75ca · outbound

This paper cites Shufflefl: Gradient- preserving federated learning using trusted execution environment,.

DIST-FL: Enhancing Security for TEE-based Aggregation in Federated Learning Shufflefl: Gradient- preserving federated learning using trusted execution environment,

Reference 14

Resolution: unresolved; no resolver link, observed 2026-06-28T05:46:08.181285Z

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Observation 0aacd406-1e0c-4f93-a812-d0d310d0ec83 · outbound

This paper cites When federated learning meets blockchain: A new distributed learning paradigm,.

DIST-FL: Enhancing Security for TEE-based Aggregation in Federated Learning When federated learning meets blockchain: A new distributed learning paradigm,

Reference 15

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Observation f6f2c2ca-5e25-4a3f-9fdd-46072f0e6983 · outbound

This paper cites Client Selection in Federated Learning: Convergence Analysis and Power-of-Choice Selection Strategies.

DIST-FL: Enhancing Security for TEE-based Aggregation in Federated Learning Client Selection in Federated Learning: Convergence Analysis and Power-of-Choice Selection Strategies

Reference 16

Resolution: verified exact; arxiv_id, observed 2026-07-02T08:46:49.027250Z

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Observation 3fdde848-f6c8-43b3-b8c8-eb7ac0f4ad6c · outbound

This paper cites Lotto: Secure participant selection against adversarial servers in federated learning,.

DIST-FL: Enhancing Security for TEE-based Aggregation in Federated Learning Lotto: Secure participant selection against adversarial servers in federated learning,

Reference 17

Resolution: unresolved; no resolver link, observed 2026-06-28T05:46:08.181285Z

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Observation 9f6fc6b9-0ba0-4a46-80ce-d34ab232f6ea · outbound

This paper cites Lotto: Secure Participant Selection against Adversarial Servers in Federated Learning.

DIST-FL: Enhancing Security for TEE-based Aggregation in Federated Learning Lotto: Secure Participant Selection against Adversarial Servers in Federated Learning

Reference 18

Resolution: verified exact; arxiv_id, observed 2026-07-02T08:46:49.034345Z

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No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 0956eba8-8426-4531-a6b1-cf99c2c58a41 · outbound

This paper cites Towards understanding biased client selection in federated learning,.

DIST-FL: Enhancing Security for TEE-based Aggregation in Federated Learning Towards understanding biased client selection in federated learning,

Reference 19

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Observation 6759e7cb-0517-48b0-b625-3c95be0c4f8b · outbound

This paper cites ROTE: Rollback protection for trusted execution,.

DIST-FL: Enhancing Security for TEE-based Aggregation in Federated Learning ROTE: Rollback protection for trusted execution,

Reference 20

Resolution: unresolved; no resolver link, observed 2026-06-28T05:46:08.181285Z

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Observation 75d995a8-3871-4a6e-93b3-6f289e7df82b · outbound

This paper cites Narrator: Secure and practical state continuity for trusted execution in the cloud,.

DIST-FL: Enhancing Security for TEE-based Aggregation in Federated Learning Narrator: Secure and practical state continuity for trusted execution in the cloud,

Reference 21

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Observation 0eba57c8-4019-4cd8-bf2f-1d3f3817cd48 · outbound

This paper cites Nimble: Rollback protection for confidential cloud services,.

DIST-FL: Enhancing Security for TEE-based Aggregation in Federated Learning Nimble: Rollback protection for confidential cloud services,

Reference 22

Resolution: unresolved; no resolver link, observed 2026-06-28T05:46:08.181285Z

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Observation 239a7016-32bc-4c32-8a11-2a8d954279e7 · outbound

This paper cites Client selection in federated learning: Principles, challenges, and opportunities,.

DIST-FL: Enhancing Security for TEE-based Aggregation in Federated Learning Client selection in federated learning: Principles, challenges, and opportunities,

Reference 23

Resolution: unresolved; no resolver link, observed 2026-06-28T05:46:08.181285Z

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Observation 0cafeaec-3545-4ca1-81a6-c14729d9570a · outbound

This paper cites Client selection for federated learning with heterogeneous resources in mobile edge,.

DIST-FL: Enhancing Security for TEE-based Aggregation in Federated Learning Client selection for federated learning with heterogeneous resources in mobile edge,

Reference 24

Resolution: unresolved; no resolver link, observed 2026-06-28T05:46:08.181285Z

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Observation 88a9a78c-9cac-4ec7-8cc9-7d8c80f907ac · outbound

This paper cites Towards understanding biased client selection in federated learning,.

DIST-FL: Enhancing Security for TEE-based Aggregation in Federated Learning Towards understanding biased client selection in federated learning,

Reference 25

Resolution: unresolved; no resolver link, observed 2026-06-28T05:46:08.181285Z

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Observation b7e36750-9203-473b-9213-9fbf3e0e2dc3 · outbound

This paper cites Google workshop on federated learning and analytics,.

DIST-FL: Enhancing Security for TEE-based Aggregation in Federated Learning Google workshop on federated learning and analytics,

Reference 26

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Observation d1a94edd-80c1-407c-a4be-0b720f524f26 · outbound

This paper cites Meta-fl: A novel meta-learning framework for optimizing heterogeneous model aggregation in federated learning,.

DIST-FL: Enhancing Security for TEE-based Aggregation in Federated Learning Meta-fl: A novel meta-learning framework for optimizing heterogeneous model aggregation in federated learning,

Reference 27

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Observation 0056a225-934f-4167-a58d-88a3c664520d · outbound

This paper cites Meta-FL: A Novel Meta-Learning Framework for Optimizing Heterogeneous Model Aggregation in Federated Learning.

DIST-FL: Enhancing Security for TEE-based Aggregation in Federated Learning Meta-FL: A Novel Meta-Learning Framework for Optimizing Heterogeneous Model Aggregation in Federated Learning

Reference 28

Resolution: verified exact; arxiv_id, observed 2026-07-02T08:46:49.031941Z

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Observation 4ae375fe-2f45-47f9-bb00-6256e8e9dd84 · outbound

This paper cites Local model poisoning attacks to byzantine-robust federated learning.

DIST-FL: Enhancing Security for TEE-based Aggregation in Federated Learning Local model poisoning attacks to byzantine-robust federated learning

Reference 29

Resolution: unresolved; no resolver link, observed 2026-06-28T05:46:08.181285Z

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Observation 4103c278-c2b8-4d0a-950e-6caa5cc743b5 · outbound

This paper cites Back to the drawing board: A critical evaluation of poisoning attacks on production federated learning,.

DIST-FL: Enhancing Security for TEE-based Aggregation in Federated Learning Back to the drawing board: A critical evaluation of poisoning attacks on production federated learning,

Reference 30

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Observation fe43af1b-a292-4b08-962c-cca21063d1ca · outbound

This paper cites Data poisoning attacks against federated learning systems,.

DIST-FL: Enhancing Security for TEE-based Aggregation in Federated Learning Data poisoning attacks against federated learning systems,

Reference 31

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Observation a1c7bc56-3e3b-49b8-a7dc-6e67d4f4ab8e · outbound

This paper cites How to backdoor federated learning,.

DIST-FL: Enhancing Security for TEE-based Aggregation in Federated Learning How to backdoor federated learning,

Reference 32

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Observation 694391d4-4be3-4e17-877f-f542ebf50a60 · outbound

This paper cites Comprehensive privacy analysis of deep learning: Passive and active white-box inference attacks against centralized and federated learning,.

DIST-FL: Enhancing Security for TEE-based Aggregation in Federated Learning Comprehensive privacy analysis of deep learning: Passive and active white-box inference attacks against centralized and federated learning,

Reference 33

Resolution: unresolved; no resolver link, observed 2026-06-28T05:46:08.181285Z

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Observation 860f278b-3645-4ffe-a435-8f65469ef88e · outbound

This paper cites Source inference attacks in federated learning,.

DIST-FL: Enhancing Security for TEE-based Aggregation in Federated Learning Source inference attacks in federated learning,

Reference 34

Resolution: unresolved; no resolver link, observed 2026-06-28T05:46:08.181285Z

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Observation 0fdc9b31-8541-4337-a990-1b3dcece7522 · outbound

This paper cites Ex- ploiting unintended property leakage in blockchain-assisted federated learning for intelligent edge computing,.

DIST-FL: Enhancing Security for TEE-based Aggregation in Federated Learning Ex- ploiting unintended property leakage in blockchain-assisted federated learning for intelligent edge computing,

Reference 35

Resolution: unresolved; no resolver link, observed 2026-06-28T05:46:08.181285Z

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Observation 2c3aefd1-45e3-4d02-8fb8-666bde40b40f · outbound

This paper cites Exploiting unintended feature leakage in collaborative learning,.

DIST-FL: Enhancing Security for TEE-based Aggregation in Federated Learning Exploiting unintended feature leakage in collaborative learning,

Reference 36

Resolution: unresolved; no resolver link, observed 2026-06-28T05:46:08.181285Z

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Observation 16e4c746-23d7-4494-b826-fd5d19909774 · outbound

This paper cites Inverting gradients - how easy is it to break privacy in federated learning?.

DIST-FL: Enhancing Security for TEE-based Aggregation in Federated Learning Inverting gradients - how easy is it to break privacy in federated learning?

Reference 37

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Observation a3ea8f01-ca88-4ec8-8e70-b296d0d9e90c · outbound

This paper cites Ma- chine learning with adversaries: Byzantine tolerant gradient descent,.

DIST-FL: Enhancing Security for TEE-based Aggregation in Federated Learning Ma- chine learning with adversaries: Byzantine tolerant gradient descent,

Reference 38

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Observation fa9ecb02-b389-40b4-bb34-59b7dbbc01c6 · outbound

This paper cites Taspinar, M.

DIST-FL: Enhancing Security for TEE-based Aggregation in Federated Learning Taspinar, M

Reference 39

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No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 475ca134-5832-4d60-a089-5840006ec26a · outbound

This paper cites Practical secure aggregation for privacy-preserving machine learning,.

DIST-FL: Enhancing Security for TEE-based Aggregation in Federated Learning Practical secure aggregation for privacy-preserving machine learning,

Reference 40

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Observation 53314f6f-a051-4d1c-8c46-88141927e715 · outbound

This paper cites Vfl: A verifiable federated learning with privacy-preserving for big data in industrial iot,.

DIST-FL: Enhancing Security for TEE-based Aggregation in Federated Learning Vfl: A verifiable federated learning with privacy-preserving for big data in industrial iot,

Reference 41

Resolution: unresolved; no resolver link, observed 2026-06-28T05:46:08.181285Z

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Observation 19c8f6af-93ec-4bb0-bfb1-a0a282249618 · outbound

This paper cites FastSecAgg: Scalable Secure Aggregation for Privacy-Preserving Federated Learning.

DIST-FL: Enhancing Security for TEE-based Aggregation in Federated Learning FastSecAgg: Scalable Secure Aggregation for Privacy-Preserving Federated Learning

Reference 42

Resolution: verified exact; arxiv_id, observed 2026-07-02T08:46:49.035925Z

Source-reported events for the cited work

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Observation 99922e7e-14a3-44bf-8027-1390bcb2975a · outbound

This paper cites Turbo-aggregate: Breaking the quadratic aggregation barrier in secure federated learning,.

DIST-FL: Enhancing Security for TEE-based Aggregation in Federated Learning Turbo-aggregate: Breaking the quadratic aggregation barrier in secure federated learning,

Reference 43

Resolution: unresolved; no resolver link, observed 2026-06-28T05:46:08.181285Z

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Observation 138d2360-8371-448f-8d1b-026e82e39314 · outbound

This paper cites Practical secure aggregation for privacy-preserving machine learning.

DIST-FL: Enhancing Security for TEE-based Aggregation in Federated Learning Practical secure aggregation for privacy-preserving machine learning

Reference 44

Resolution: unresolved; no resolver link, observed 2026-06-28T05:46:08.181285Z

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Observation 0583d90e-c948-4af4-9098-139a38fad5b1 · outbound

This paper cites Prio: Private, robust, and scalable computation of aggregate statistics,.

DIST-FL: Enhancing Security for TEE-based Aggregation in Federated Learning Prio: Private, robust, and scalable computation of aggregate statistics,

Reference 45

Resolution: unresolved; no resolver link, observed 2026-06-28T05:46:08.181285Z

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Observation 6fb9951e-92c6-4b3d-b567-1daf11c7dd26 · outbound

This paper cites Giving state to the stateless: Augmenting trustworthy computation with ledgers,.

DIST-FL: Enhancing Security for TEE-based Aggregation in Federated Learning Giving state to the stateless: Augmenting trustworthy computation with ledgers,

Reference 46

Resolution: unresolved; no resolver link, observed 2026-06-28T05:46:08.181285Z

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Observation 60863a49-31c4-491c-886e-ee2478eb92de · outbound

This paper cites Idea: State-continuous transfer of state in protected-module architectures,.

DIST-FL: Enhancing Security for TEE-based Aggregation in Federated Learning Idea: State-continuous transfer of state in protected-module architectures,

Reference 47

Resolution: unresolved; no resolver link, observed 2026-06-28T05:46:08.181285Z

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Observation 78483879-ab69-4d08-aacb-aa9078b0ee2f · outbound

This paper cites ADAM-CS: Advanced asynchronous monotonic counter service,.

DIST-FL: Enhancing Security for TEE-based Aggregation in Federated Learning ADAM-CS: Advanced asynchronous monotonic counter service,

Reference 48

Resolution: unresolved; no resolver link, observed 2026-06-28T05:46:08.181285Z

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Observation a5c11168-190b-4893-906a-782f79b2062d · outbound

This paper cites Learning multiple layers of features from tiny images,.

DIST-FL: Enhancing Security for TEE-based Aggregation in Federated Learning Learning multiple layers of features from tiny images,

Reference 49

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Observation fb616d2f-dd4c-4b07-8fe6-341c114606e2 · outbound

This paper cites Time-sensitive learning for heterogeneous federated edge intelligence,.

DIST-FL: Enhancing Security for TEE-based Aggregation in Federated Learning Time-sensitive learning for heterogeneous federated edge intelligence,

Reference 50

Resolution: verified exact; arxiv_id, observed 2026-06-28T05:51:40.608048Z

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Observation 41455d3d-e236-4247-81de-0cf1d6fc232d · outbound

This paper cites Olive: Oblivious federated learning on trusted execution environment against the risk of sparsification,.

DIST-FL: Enhancing Security for TEE-based Aggregation in Federated Learning Olive: Oblivious federated learning on trusted execution environment against the risk of sparsification,

Reference 51

Resolution: unresolved; no resolver link, observed 2026-06-28T05:46:08.181285Z

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Observation 23329f87-6048-44ce-8365-a23e9d9e2d94 · outbound

This paper cites Papaya: Practical, private, and scalable federated learning,.

DIST-FL: Enhancing Security for TEE-based Aggregation in Federated Learning Papaya: Practical, private, and scalable federated learning,

Reference 52

Resolution: unresolved; no resolver link, observed 2026-06-28T05:46:08.181285Z

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Observation 5354683d-c7bc-47e3-a1a3-61ea5fce6513 · outbound

This paper cites Deta: Minimizing data leaks in federated learning via decentralized and trustworthy aggregation,.

DIST-FL: Enhancing Security for TEE-based Aggregation in Federated Learning Deta: Minimizing data leaks in federated learning via decentralized and trustworthy aggregation,

Reference 53

Resolution: unresolved; no resolver link, observed 2026-06-28T05:46:08.181285Z

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Observation 7f56702a-21c2-4f85-9388-1020435a2f4f · outbound

This paper cites Confidential Federated Computations.

DIST-FL: Enhancing Security for TEE-based Aggregation in Federated Learning Confidential Federated Computations

Reference 54

Resolution: metadata mismatch; arxiv_id, observed 2026-07-02T08:46:49.046997Z

Source-reported events for the cited work

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Observation 727f0918-8f21-43ac-b906-51e378e4a664 · outbound

This paper cites The sybil attack,.

DIST-FL: Enhancing Security for TEE-based Aggregation in Federated Learning The sybil attack,

Reference 55

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Observation abfdbee1-075a-4fcb-91f1-ced2ba03b03e · outbound

This paper cites Brief announcement: Byzantine-tolerant machine learning,.

DIST-FL: Enhancing Security for TEE-based Aggregation in Federated Learning Brief announcement: Byzantine-tolerant machine learning,

Reference 56

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Observation 64f7fb1e-16d1-48f8-bc92-532b96145726 · outbound

This paper cites Practical byzantine fault tolerance,.

DIST-FL: Enhancing Security for TEE-based Aggregation in Federated Learning Practical byzantine fault tolerance,

Reference 57

Resolution: unresolved; no resolver link, observed 2026-06-28T05:46:08.181285Z

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Observation c41abd3e-6c8b-45c0-a170-5d9933279a4a · outbound

This paper cites HotStuff: BFT consensus with linearity and responsiveness,.

DIST-FL: Enhancing Security for TEE-based Aggregation in Federated Learning HotStuff: BFT consensus with linearity and responsiveness,

Reference 58

Resolution: unresolved; no resolver link, observed 2026-06-28T05:46:08.181285Z

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Observation fa6b13da-f353-45bc-9093-18994cb1f010 · outbound

This paper cites Fast-HotStuff: A fast and robust bft protocol for blockchains,.

DIST-FL: Enhancing Security for TEE-based Aggregation in Federated Learning Fast-HotStuff: A fast and robust bft protocol for blockchains,

Reference 59

Resolution: unresolved; no resolver link, observed 2026-06-28T05:46:08.181285Z

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Observation fe1cd4c6-2b0c-49ee-8bf3-182384ff2302 · outbound

This paper cites Ladon: High-Performance Multi-BFT Consensus via Dynamic Global Ordering,.

DIST-FL: Enhancing Security for TEE-based Aggregation in Federated Learning Ladon: High-Performance Multi-BFT Consensus via Dynamic Global Ordering,

Reference 60

Resolution: unresolved; no resolver link, observed 2026-06-28T05:46:08.181285Z

Source-reported events for the cited work

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Observation e6986fa6-e5c6-4d22-8a5b-d163f8ccd402 · outbound

This paper cites Byzantine-Robust and Privacy-Preserving Framework for FedML.

DIST-FL: Enhancing Security for TEE-based Aggregation in Federated Learning Byzantine-Robust and Privacy-Preserving Framework for FedML

Reference 61

Resolution: verified exact; arxiv_id, observed 2026-07-02T08:46:49.044681Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 0575596f-157f-4a15-8f44-4539deee1abe · outbound

This paper cites Damysus: Streamlined BFT consensus leveraging trusted components,.

DIST-FL: Enhancing Security for TEE-based Aggregation in Federated Learning Damysus: Streamlined BFT consensus leveraging trusted components,

Reference 62

Resolution: unresolved; no resolver link, observed 2026-06-28T05:46:08.181285Z

Source-reported events for the cited work

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Observation cf4d85ff-3179-4732-83fe-3c7587731f26 · outbound

This paper cites Salticidae: minimal C++ asynchronous network library,.

DIST-FL: Enhancing Security for TEE-based Aggregation in Federated Learning Salticidae: minimal C++ asynchronous network library,

Reference 63

Resolution: unresolved; no resolver link, observed 2026-06-28T05:46:08.181285Z

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Observation fa66ab4a-c257-4d9c-96c3-992f5c88d8c5 · outbound

This paper cites Secretflow: A unified framework for privacy-preserving data analysis and machine learning,.

DIST-FL: Enhancing Security for TEE-based Aggregation in Federated Learning Secretflow: A unified framework for privacy-preserving data analysis and machine learning,

Reference 64

Resolution: unresolved; no resolver link, observed 2026-06-28T05:46:08.181285Z

Source-reported events for the cited work

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Observation 83bcc115-64b8-4b86-8c95-bbadef1e3033 · outbound

This paper cites {FLAME}: Taming backdoors in federated learning,.

DIST-FL: Enhancing Security for TEE-based Aggregation in Federated Learning {FLAME}: Taming backdoors in federated learning,

Reference 65

Resolution: unresolved; no resolver link, observed 2026-06-28T05:46:08.181285Z

Source-reported events for the cited work

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Observation cd3f4d8d-da3d-4720-8e9c-b969068d6fc4 · outbound

This paper cites Fldetector: Defending federated learning against model poisoning attacks via detecting ma- licious clients,.

DIST-FL: Enhancing Security for TEE-based Aggregation in Federated Learning Fldetector: Defending federated learning against model poisoning attacks via detecting ma- licious clients,

Reference 66

Resolution: unresolved; no resolver link, observed 2026-06-28T05:46:08.181285Z

Source-reported events for the cited work

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Observation 96348696-867d-4632-a6fd-f8b4a0d7a3af · outbound

This paper cites Fedrecover: Recovering from poisoning attacks in federated learning using historical information,.

DIST-FL: Enhancing Security for TEE-based Aggregation in Federated Learning Fedrecover: Recovering from poisoning attacks in federated learning using historical information,

Reference 67

Resolution: unresolved; no resolver link, observed 2026-06-28T05:46:08.181285Z

Source-reported events for the cited work

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Observation 6d0ea981-9622-4644-b517-b3dafcec80b7 · outbound

This paper cites Fedredefense: Defending against model poisoning attacks for federated learning using model update reconstruction error.

DIST-FL: Enhancing Security for TEE-based Aggregation in Federated Learning Fedredefense: Defending against model poisoning attacks for federated learning using model update reconstruction error

Reference 68

Resolution: unresolved; no resolver link, observed 2026-06-28T05:46:08.181285Z

Source-reported events for the cited work

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Observation 8813a974-0d43-4cd7-9c70-335131caefee · outbound

This paper cites Boba: Byzantine-robust federated learning with label skewness,.

DIST-FL: Enhancing Security for TEE-based Aggregation in Federated Learning Boba: Byzantine-robust federated learning with label skewness,

Reference 69

Resolution: unresolved; no resolver link, observed 2026-06-28T05:46:08.181285Z

Source-reported events for the cited work

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Observation e8b52a72-f0d1-4cd8-8412-8eecbb02de49 · outbound

This paper cites An experimental study of byzantine- robust aggregation schemes in federated learning,.

DIST-FL: Enhancing Security for TEE-based Aggregation in Federated Learning An experimental study of byzantine- robust aggregation schemes in federated learning,

Reference 70

Resolution: unresolved; no resolver link, observed 2026-06-28T05:46:08.181285Z

Source-reported events for the cited work

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Observation d20eec1a-d444-40c7-a0b1-13f8c3b1b0f5 · outbound

This paper cites Differentially Private Federated Learning: A Client Level Perspective.

DIST-FL: Enhancing Security for TEE-based Aggregation in Federated Learning Differentially Private Federated Learning: A Client Level Perspective

Reference 71

Resolution: verified exact; local_arxiv, observed 2026-07-02T08:46:49.029587Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 7ff073fa-ac4a-42c5-b814-d5ed026c9196 · outbound

This paper cites Federated learning with differential privacy: Algorithms 16 and performance analysis,.

DIST-FL: Enhancing Security for TEE-based Aggregation in Federated Learning Federated learning with differential privacy: Algorithms 16 and performance analysis,

Reference 72

Resolution: unresolved; no resolver link, observed 2026-06-28T05:46:08.181285Z

Source-reported events for the cited work

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Observation 5bb22763-5f54-4a62-98ec-06d90454221b · outbound

This paper cites Local and Central Differential Privacy for Robustness and Privacy in Federated Learning.

DIST-FL: Enhancing Security for TEE-based Aggregation in Federated Learning Local and Central Differential Privacy for Robustness and Privacy in Federated Learning

Reference 73

Resolution: verified exact; arxiv_id, observed 2026-07-02T08:46:49.024588Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation e34cbb9f-ff51-4a5c-88ce-131f847aab2c · outbound

This paper cites Bvdfed: Byzantine-resilient and verifiable aggregation for differentially private federated learning,.

DIST-FL: Enhancing Security for TEE-based Aggregation in Federated Learning Bvdfed: Byzantine-resilient and verifiable aggregation for differentially private federated learning,

Reference 74

Resolution: unresolved; no resolver link, observed 2026-06-28T05:46:08.181285Z

Source-reported events for the cited work

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Observation 38ddd3e4-cb5b-4ca0-86cf-61635d8b358d · outbound

This paper cites SecFL: Confidential Federated Learning using TEEs.

DIST-FL: Enhancing Security for TEE-based Aggregation in Federated Learning SecFL: Confidential Federated Learning using TEEs

Reference 75

Resolution: verified exact; arxiv_id, observed 2026-07-02T08:46:49.019210Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation a900373b-c22b-4957-9336-d966bd660cb1 · outbound

This paper cites Federated Learning: Opportunities and Challenges.

DIST-FL: Enhancing Security for TEE-based Aggregation in Federated Learning Federated Learning: Opportunities and Challenges

Reference 76

Resolution: metadata mismatch; arxiv_id, observed 2026-07-02T08:46:49.036906Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation dd44b0c3-233c-45e2-8b79-a1449a4900b5 · outbound

This paper cites Machine Learning with Confidential Computing: A Systematization of Knowledge.

DIST-FL: Enhancing Security for TEE-based Aggregation in Federated Learning Machine Learning with Confidential Computing: A Systematization of Knowledge

Reference 77

Resolution: verified exact; arxiv_id, observed 2026-07-02T08:46:49.039453Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation fcff4d91-db2d-4a38-b208-e1c16409b19f · outbound

This paper cites Enabling execution assurance of federated learning at untrusted participants,.

DIST-FL: Enhancing Security for TEE-based Aggregation in Federated Learning Enabling execution assurance of federated learning at untrusted participants,

Reference 78

Resolution: unresolved; no resolver link, observed 2026-06-28T05:46:08.181285Z

Source-reported events for the cited work

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Observation 37b4ada9-8e38-4d6f-b018-742862d2397a · outbound

This paper cites Privacy-preserving and robust federated deep metric learning,.

DIST-FL: Enhancing Security for TEE-based Aggregation in Federated Learning Privacy-preserving and robust federated deep metric learning,

Reference 79

Resolution: verified exact; arxiv_id, observed 2026-06-28T05:51:40.610691Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation b2e0905d-42a4-4bc0-b6a4-51ff40fb02d8 · outbound

This paper cites Kwon, Jagmohan Chauhan, Abhishek Kumar, Pan Hui HKUST, and Cecilia Mascolo.

DIST-FL: Enhancing Security for TEE-based Aggregation in Federated Learning Kwon, Jagmohan Chauhan, Abhishek Kumar, Pan Hui HKUST, and Cecilia Mascolo

Reference 80

Resolution: metadata mismatch; arxiv_id, observed 2026-06-28T05:51:40.615278Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation c234ec1a-919f-4063-9f79-171e533eaf21 · outbound

This paper cites A performance analysis of vm-based trusted execution environments for confidential federated learning,.

DIST-FL: Enhancing Security for TEE-based Aggregation in Federated Learning A performance analysis of vm-based trusted execution environments for confidential federated learning,

Reference 81

Resolution: unresolved; no resolver link, observed 2026-06-28T05:46:08.181285Z

Source-reported events for the cited work

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Observation e29f9c9d-980b-4723-9ddf-ca2fcf8dfcd6 · outbound

This paper cites SGX documentation: SGX create monotonic counter,.

DIST-FL: Enhancing Security for TEE-based Aggregation in Federated Learning SGX documentation: SGX create monotonic counter,

Reference 82

Resolution: unresolved; no resolver link, observed 2026-06-28T05:46:08.181285Z

Source-reported events for the cited work

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Observation dce6afd6-329b-4cc0-a924-2f823dcd9c75 · outbound

This paper cites Ariadne: A minimal approach to state continuity,.

DIST-FL: Enhancing Security for TEE-based Aggregation in Federated Learning Ariadne: A minimal approach to state continuity,

Reference 83

Resolution: unresolved; no resolver link, observed 2026-06-28T05:46:08.181285Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3533e041-a8b2-4be1-ac17-8444f050d162 · outbound

This paper cites ICE: A passive, high-speed, state-continuity scheme,.

DIST-FL: Enhancing Security for TEE-based Aggregation in Federated Learning ICE: A passive, high-speed, state-continuity scheme,

Reference 84

Resolution: unresolved; no resolver link, observed 2026-06-28T05:46:08.181285Z

Source-reported events for the cited work

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Observation 4edba89a-dc7b-481e-b068-d61800699eef · outbound

This paper cites Memoir: Practical state continuity for protected modules,.

DIST-FL: Enhancing Security for TEE-based Aggregation in Federated Learning Memoir: Practical state continuity for protected modules,

Reference 85

Resolution: unresolved; no resolver link, observed 2026-06-28T05:46:08.181285Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 074ed365-ed81-4593-80b8-6ac0a30e632c · outbound

This paper cites Ensuring state continuity for confidential computing: A blockchain-based approach,.

DIST-FL: Enhancing Security for TEE-based Aggregation in Federated Learning Ensuring state continuity for confidential computing: A blockchain-based approach,

Reference 86

Resolution: unresolved; no resolver link, observed 2026-06-28T05:46:08.181285Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a214e82f-508a-4d59-92aa-65404304ad59 · outbound

This paper cites Formally verifying a rollback-prevention protocol for TEEs,.

DIST-FL: Enhancing Security for TEE-based Aggregation in Federated Learning Formally verifying a rollback-prevention protocol for TEEs,

Reference 87

Resolution: unresolved; no resolver link, observed 2026-06-28T05:46:08.181285Z

Source-reported events for the cited work

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Observation 350e970e-42c4-40ae-94ea-7722b3642363 · outbound

This paper cites Engraft: Enclave-guarded raft on byzantine faulty nodes,.

DIST-FL: Enhancing Security for TEE-based Aggregation in Federated Learning Engraft: Enclave-guarded raft on byzantine faulty nodes,

Reference 88

Resolution: unresolved; no resolver link, observed 2026-06-28T05:46:08.181285Z

Source-reported events for the cited work

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Observation 7ea5cd2c-77c3-4153-9e46-b377fb655ed0 · outbound

This paper cites Rollback and forking detection for trusted execution environments using lightweight collective memory,.

DIST-FL: Enhancing Security for TEE-based Aggregation in Federated Learning Rollback and forking detection for trusted execution environments using lightweight collective memory,

Reference 89

Resolution: unresolved; no resolver link, observed 2026-06-28T05:46:08.181285Z

Source-reported events for the cited work

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Observation 41f3408d-c256-48a5-b1c5-8c8f26d582bd · outbound

This paper cites Crisp: Confidentiality, rollback, and integrity storage protection for confidential cloud-native computing,.

DIST-FL: Enhancing Security for TEE-based Aggregation in Federated Learning Crisp: Confidentiality, rollback, and integrity storage protection for confidential cloud-native computing,

Reference 90

Resolution: unresolved; no resolver link, observed 2026-06-28T05:46:08.181285Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3d4f6be0-21d5-4593-8058-023f6f4e05f2 · outbound

This paper cites Tee is not a healer: Rollback-resistant reliable storage,.

DIST-FL: Enhancing Security for TEE-based Aggregation in Federated Learning Tee is not a healer: Rollback-resistant reliable storage,

Reference 91

Resolution: unresolved; no resolver link, observed 2026-06-28T05:46:08.181285Z

Source-reported events for the cited work

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Observation e373ac89-7083-4b00-a86a-e08283ea3343 · outbound

This paper cites Rollbaccine: Herd immunity against storage rollback attacks in tees,.

DIST-FL: Enhancing Security for TEE-based Aggregation in Federated Learning Rollbaccine: Herd immunity against storage rollback attacks in tees,

Reference 92

Resolution: unresolved; no resolver link, observed 2026-06-28T05:46:08.181285Z

Source-reported events for the cited work

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Observation 2ed0b629-83a6-43ab-99cb-ee9c68bca77f · outbound

This paper cites Achilles: Efficient TEE-Assisted BFT Consensus via Rollback Resilient Recovery,.

DIST-FL: Enhancing Security for TEE-based Aggregation in Federated Learning Achilles: Efficient TEE-Assisted BFT Consensus via Rollback Resilient Recovery,

Reference 93

Resolution: unresolved; no resolver link, observed 2026-06-28T05:46:08.181285Z

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