Efficient Privacy-Preserving Cross-Silo Federated Learning with Multi-Key Homomorphic Encryption | Paper Citation Record

Observation 4255bbce-f3df-456a-894a-67d8ebccc75c · outbound

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

Efficient Privacy-Preserving Cross-Silo Federated Learning with Multi-Key Homomorphic Encryption Advances and open problems in federated learning,

Reference 1

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Observation c036e633-5cbe-4b50-8e86-5c1b90ce458a · outbound

This paper cites Deep leakage from gradients,.

Efficient Privacy-Preserving Cross-Silo Federated Learning with Multi-Key Homomorphic Encryption Deep leakage from gradients,

Reference 2

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Observation d5d9b51a-dd5d-4421-b79b-98dd27faccb9 · outbound

This paper cites See through gradients: Image batch recovery via grad- inversion,.

Efficient Privacy-Preserving Cross-Silo Federated Learning with Multi-Key Homomorphic Encryption See through gradients: Image batch recovery via grad- inversion,

Reference 3

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Observation 73c56b5a-ba89-4679-9b19-219be0cc9f95 · outbound

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

Efficient Privacy-Preserving Cross-Silo Federated Learning with Multi-Key Homomorphic Encryption Inverting gradients-how easy is it to break privacy in federated learning?

Reference 4

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Observation 47e9bb50-038a-4cb3-809d-92acc1d288c7 · outbound

This paper cites Federated learning with differential privacy: Algo- rithms and performance analysis,.

Efficient Privacy-Preserving Cross-Silo Federated Learning with Multi-Key Homomorphic Encryption Federated learning with differential privacy: Algo- rithms and performance analysis,

Reference 5

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Observation 62604cee-9d09-4a48-9a82-9d77231fc03a · outbound

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

Efficient Privacy-Preserving Cross-Silo Federated Learning with Multi-Key Homomorphic Encryption Practical secure aggre- gation for privacy-preserving machine learning,

Reference 6

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Observation 0afb40cc-5386-4e82-9aac-21159661ac6c · outbound

This paper cites Batchcrypt: Efficient homomorphic encryption for cross-silo federated learning,.

Efficient Privacy-Preserving Cross-Silo Federated Learning with Multi-Key Homomorphic Encryption Batchcrypt: Efficient homomorphic encryption for cross-silo federated learning,

Reference 7

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Observation c84fe4b5-00ad-41f0-8770-a20508c856c7 · outbound

This paper cites FedML-HE: An efficient homomorphic-encryption-based privacy-preserving federated learning system,.

Efficient Privacy-Preserving Cross-Silo Federated Learning with Multi-Key Homomorphic Encryption FedML-HE: An efficient homomorphic-encryption-based privacy-preserving federated learning system,

Reference 8

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Observation 97c25f6e-c788-498c-8677-cd7c6ccc298e · outbound

This paper cites Differentially private learning needs bet- ter features (or much more data),.

Efficient Privacy-Preserving Cross-Silo Federated Learning with Multi-Key Homomorphic Encryption Differentially private learning needs bet- ter features (or much more data),

Reference 9

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Observation 3d7258d9-394d-4d73-ad1b-c486f303070a · outbound

This paper cites Federated machine learning: Concept and applications,.

Efficient Privacy-Preserving Cross-Silo Federated Learning with Multi-Key Homomorphic Encryption Federated machine learning: Concept and applications,

Reference 10

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Observation 5737105a-b5ff-488d-919d-263a356b79f7 · outbound

This paper cites On data banks and privacy homomorphisms,.

Efficient Privacy-Preserving Cross-Silo Federated Learning with Multi-Key Homomorphic Encryption On data banks and privacy homomorphisms,

Reference 11

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Observation d0365c9d-59a2-4161-b599-8deaa21328cc · outbound

This paper cites Efficient and straggler-resistant homomorphic encryption for hetero- geneous federated learning,.

Efficient Privacy-Preserving Cross-Silo Federated Learning with Multi-Key Homomorphic Encryption Efficient and straggler-resistant homomorphic encryption for hetero- geneous federated learning,

Reference 12

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Observation 288771eb-602e-4129-814f-485e09cfc779 · outbound

This paper cites Maskcrypt: Federated learning with selective homomorphic encryption,.

Efficient Privacy-Preserving Cross-Silo Federated Learning with Multi-Key Homomorphic Encryption Maskcrypt: Federated learning with selective homomorphic encryption,

Reference 13

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Observation 1baa95f6-04e7-45f3-914e-ab2d34bf138f · outbound

This paper cites Secfed: A secure and efficient federated learning based on multi-key homo- morphic encryption,.

Efficient Privacy-Preserving Cross-Silo Federated Learning with Multi-Key Homomorphic Encryption Secfed: A secure and efficient federated learning based on multi-key homo- morphic encryption,

Reference 14

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Observation 40215d0a-ff65-4e6c-85f6-0d3d45d86441 · outbound

This paper cites Privacy-preserving federated learning based on multi-key homomorphic encryption,.

Efficient Privacy-Preserving Cross-Silo Federated Learning with Multi-Key Homomorphic Encryption Privacy-preserving federated learning based on multi-key homomorphic encryption,

Reference 15

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Observation 43847448-7de0-46d3-b4f2-576e9fd3c38e · outbound

This paper cites Privacy-preserving federated learning using homomorphic encryption with different encryption keys,.

Efficient Privacy-Preserving Cross-Silo Federated Learning with Multi-Key Homomorphic Encryption Privacy-preserving federated learning using homomorphic encryption with different encryption keys,

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:34:44.250886Z

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Observation a5d160ca-c2e4-428d-bfde-c3390f75f2df · outbound

This paper cites Federated optimization in heterogeneous networks,.

Efficient Privacy-Preserving Cross-Silo Federated Learning with Multi-Key Homomorphic Encryption Federated optimization in heterogeneous networks,

Reference 17

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Observation aa68c549-8cdf-4511-8cea-50dd1f80e8cc · outbound

This paper cites Learning both weights and connections for efficient neural network,.

Efficient Privacy-Preserving Cross-Silo Federated Learning with Multi-Key Homomorphic Encryption Learning both weights and connections for efficient neural network,

Reference 18

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Observation 693dd132-d736-4ea1-a9e7-e62807c7c3e8 · outbound

This paper cites Every vote counts: Ranking-Based training of federated learning to resist poisoning attacks,.

Efficient Privacy-Preserving Cross-Silo Federated Learning with Multi-Key Homomorphic Encryption Every vote counts: Ranking-Based training of federated learning to resist poisoning attacks,

Reference 19

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Observation eea44ff0-9472-49a8-a568-434c21163235 · outbound

This paper cites Cross-Silo Federated Learning: Challenges and Opportunities.

Efficient Privacy-Preserving Cross-Silo Federated Learning with Multi-Key Homomorphic Encryption Cross-Silo Federated Learning: Challenges and Opportunities

Reference 20

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Observation d65f6a98-27db-4163-884d-9c78311c6b34 · outbound

This paper cites Backpropagation and stochastic gradient descent method,.

Efficient Privacy-Preserving Cross-Silo Federated Learning with Multi-Key Homomorphic Encryption Backpropagation and stochastic gradient descent method,

Reference 21

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Observation 0b40412f-eda7-44d7-b325-1f795db1ed48 · outbound

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

Efficient Privacy-Preserving Cross-Silo Federated Learning with Multi-Key Homomorphic Encryption Communication-efficient learning of deep networks from decentral- ized data,

Reference 22

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Observation c9a1185d-0d87-470f-9176-4cfc3a4a1575 · outbound

This paper cites Federated Learning with Non-IID Data.

Efficient Privacy-Preserving Cross-Silo Federated Learning with Multi-Key Homomorphic Encryption Federated Learning with Non-IID Data

Reference 23

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Observation 2f9a8ad5-d7d4-48e0-ae1f-c666a4448617 · outbound

This paper cites On ideal lattices and learning with errors over rings,.

Efficient Privacy-Preserving Cross-Silo Federated Learning with Multi-Key Homomorphic Encryption On ideal lattices and learning with errors over rings,

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:34:43.723808Z

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Observation 2c5ad8e4-a41a-4592-8e2a-8474738ab26f · outbound

This paper cites Efficient multi-key ho- momorphic encryption with packed ciphertexts with application to oblivious neural network inference,.

Efficient Privacy-Preserving Cross-Silo Federated Learning with Multi-Key Homomorphic Encryption Efficient multi-key ho- momorphic encryption with packed ciphertexts with application to oblivious neural network inference,

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:34:43.575152Z

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Observation 1af78057-9ec3-47e6-9709-ed0f81516fd3 · outbound

This paper cites Second order derivatives for network pruning: Optimal brain surgeon,.

Efficient Privacy-Preserving Cross-Silo Federated Learning with Multi-Key Homomorphic Encryption Second order derivatives for network pruning: Optimal brain surgeon,

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:34:43.440505Z

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Observation f15f0ca2-f1b9-4858-8efb-d953c0c79048 · outbound

This paper cites Optimal brain damage,.

Efficient Privacy-Preserving Cross-Silo Federated Learning with Multi-Key Homomorphic Encryption Optimal brain damage,

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:34:43.251340Z

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Observation 797bf14a-98aa-4884-842b-a20d75e98e58 · outbound

This paper cites Lookahead: A far- sighted alternative of magnitude-based pruning,.

Efficient Privacy-Preserving Cross-Silo Federated Learning with Multi-Key Homomorphic Encryption Lookahead: A far- sighted alternative of magnitude-based pruning,

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:34:43.096590Z

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Observation 52b21aaf-e121-4ead-8a8c-0153001849c0 · outbound

This paper cites The State of Sparsity in Deep Neural Networks.

Efficient Privacy-Preserving Cross-Silo Federated Learning with Multi-Key Homomorphic Encryption The State of Sparsity in Deep Neural Networks

Reference 29

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Observation 3b05adda-4d45-4ac9-9ee4-c919c960e31f · outbound

This paper cites SNIP: Single-shot Network Pruning based on Connection Sensitivity.

Efficient Privacy-Preserving Cross-Silo Federated Learning with Multi-Key Homomorphic Encryption SNIP: Single-shot Network Pruning based on Connection Sensitivity

Reference 30

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Observation 80bc991e-b43c-4815-ab48-766998a16114 · outbound

This paper cites Picking Winning Tickets Before Training by Preserving Gradient Flow.

Efficient Privacy-Preserving Cross-Silo Federated Learning with Multi-Key Homomorphic Encryption Picking Winning Tickets Before Training by Preserving Gradient Flow

Reference 31

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Observation 83573e24-ba9f-4a8b-8bb3-c4c2722599e1 · outbound

This paper cites Pruning neural networks without any data by iteratively conserving synaptic flow,.

Efficient Privacy-Preserving Cross-Silo Federated Learning with Multi-Key Homomorphic Encryption Pruning neural networks without any data by iteratively conserving synaptic flow,

Reference 32

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Observation 6d55e32f-af45-49d9-bc66-ca1548b0133f · outbound

This paper cites A survey on deep neural network pruning: Taxonomy, comparison, analysis, and recommendations,.

Efficient Privacy-Preserving Cross-Silo Federated Learning with Multi-Key Homomorphic Encryption A survey on deep neural network pruning: Taxonomy, comparison, analysis, and recommendations,

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:34:42.725486Z

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Observation d68463eb-2cf9-4861-9d5a-923b3de8b43a · outbound

This paper cites Revisiting sparsity hunting in federated learning: Why does sparsity consensus matter?.

Efficient Privacy-Preserving Cross-Silo Federated Learning with Multi-Key Homomorphic Encryption Revisiting sparsity hunting in federated learning: Why does sparsity consensus matter?

Reference 34

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Observation 89bde9c2-e330-491e-a945-bbb45b7b6d82 · outbound

This paper cites A Masked Pruning Approach for Dimensionality Reduction in Communication-Efficient Federated Learning Systems.

Efficient Privacy-Preserving Cross-Silo Federated Learning with Multi-Key Homomorphic Encryption A Masked Pruning Approach for Dimensionality Reduction in Communication-Efficient Federated Learning Systems

Reference 35

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Observation 8643b569-91f4-4616-abe1-87fb5505ad36 · outbound

This paper cites Privacy-preserving deep learning via additively homomorphic encryption,.

Efficient Privacy-Preserving Cross-Silo Federated Learning with Multi-Key Homomorphic Encryption Privacy-preserving deep learning via additively homomorphic encryption,

Reference 36

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Observation 091adb9b-656f-45ef-9cee-d37e20f261ab · outbound

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

Efficient Privacy-Preserving Cross-Silo Federated Learning with Multi-Key Homomorphic Encryption Practical secure aggregation for privacy- preserving machine learning,

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:34:42.203276Z

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Observation f529f762-a83d-4120-83b0-d6cfe741bf10 · outbound

This paper cites Scaffold: Stochastic controlled averaging for federated learning,.

Efficient Privacy-Preserving Cross-Silo Federated Learning with Multi-Key Homomorphic Encryption Scaffold: Stochastic controlled averaging for federated learning,

Reference 38

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This paper cites Tackling the objective inconsistency problem in heterogeneous federated optimiza- tion,.

Efficient Privacy-Preserving Cross-Silo Federated Learning with Multi-Key Homomorphic Encryption Tackling the objective inconsistency problem in heterogeneous federated optimiza- tion,

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:34:41.992614Z

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Observation 0d5026d7-8382-4f0f-9ab9-fe26a88dd963 · outbound

This paper cites Measuring the Effects of Non-Identical Data Distribution for Federated Visual Classification.

Efficient Privacy-Preserving Cross-Silo Federated Learning with Multi-Key Homomorphic Encryption Measuring the Effects of Non-Identical Data Distribution for Federated Visual Classification

Reference 40

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Observation cf2b4970-ffc5-4def-b639-a8c86bcad394 · outbound

This paper cites Gradient-based learning applied to document recognition,.

Efficient Privacy-Preserving Cross-Silo Federated Learning with Multi-Key Homomorphic Encryption Gradient-based learning applied to document recognition,

Reference 41

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Observation d4672c23-5a52-46e6-bc66-91650a579d38 · outbound

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

Efficient Privacy-Preserving Cross-Silo Federated Learning with Multi-Key Homomorphic Encryption Learning multiple layers of features from tiny images,

Reference 42

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Observation 8f60fead-3049-4395-b0c0-28b4a4c6c710 · outbound

This paper cites Supermasks in superposition,.

Efficient Privacy-Preserving Cross-Silo Federated Learning with Multi-Key Homomorphic Encryption Supermasks in superposition,

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:34:41.744408Z

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Observation 478dab56-8fd3-4ab8-a0e9-12d5ed466489 · outbound

This paper cites What’s hidden in a randomly weighted neural net- work?.

Efficient Privacy-Preserving Cross-Silo Federated Learning with Multi-Key Homomorphic Encryption What’s hidden in a randomly weighted neural net- work?

Reference 44

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Observation 62659a24-bd51-4684-8ae0-924929280826 · outbound

This paper cites Flower: A Friendly Federated Learning Research Framework.

Efficient Privacy-Preserving Cross-Silo Federated Learning with Multi-Key Homomorphic Encryption Flower: A Friendly Federated Learning Research Framework

Reference 45

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Observation 17c70af1-0437-4213-a8b5-65b85543c0e8 · outbound

This paper cites Asymptotically faster multi-key homomorphic encryption from homomorphic gadget decomposition,.

Efficient Privacy-Preserving Cross-Silo Federated Learning with Multi-Key Homomorphic Encryption Asymptotically faster multi-key homomorphic encryption from homomorphic gadget decomposition,

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:34:41.424501Z

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Observation a2d36aac-59f1-426e-9161-1a0085e34a18 · outbound

This paper cites xmkckks implementation,.

Efficient Privacy-Preserving Cross-Silo Federated Learning with Multi-Key Homomorphic Encryption xmkckks implementation,

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:34:41.182617Z

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Observation 4a081491-2b1c-4d4d-9de4-4aeab883776e · outbound

This paper cites iDLG: Improved Deep Leakage from Gradients.

Efficient Privacy-Preserving Cross-Silo Federated Learning with Multi-Key Homomorphic Encryption iDLG: Improved Deep Leakage from Gradients

Reference 48

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Observation 971059e0-2a87-43b9-933c-9251de63c7f0 · outbound

This paper cites A Framework for Evaluating Gradient Leakage Attacks in Federated Learning.

Efficient Privacy-Preserving Cross-Silo Federated Learning with Multi-Key Homomorphic Encryption A Framework for Evaluating Gradient Leakage Attacks in Federated Learning

Reference 49

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Observation 6d884aa9-6e20-472b-ada0-cfeebbaedd77 · outbound

This paper cites Fully homomorphic encryption using ideal lattices,.

Efficient Privacy-Preserving Cross-Silo Federated Learning with Multi-Key Homomorphic Encryption Fully homomorphic encryption using ideal lattices,

Reference 50

Resolution: unresolved; no resolver link, observed 2026-08-07T15:34:38.617459Z

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Observation 6ae827e4-c2b1-47a0-a0df-749a40ede613 · outbound

This paper cites Fully homomorphic encryption with relatively small key and ciphertext sizes,.

Efficient Privacy-Preserving Cross-Silo Federated Learning with Multi-Key Homomorphic Encryption Fully homomorphic encryption with relatively small key and ciphertext sizes,

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:34:40.968508Z

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Observation 1d099743-65d8-45af-9253-f70ae6da4c7c · outbound

This paper cites Demystifying bootstrapping in fully homomorphic encryption,.

Efficient Privacy-Preserving Cross-Silo Federated Learning with Multi-Key Homomorphic Encryption Demystifying bootstrapping in fully homomorphic encryption,

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:34:40.586505Z

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Observation bf76fb0d-8fa0-4113-8c6c-130458e7f17f · outbound

This paper cites Homopai: A secure collaborative machine learning platform based on homomorphic encryption,.

Efficient Privacy-Preserving Cross-Silo Federated Learning with Multi-Key Homomorphic Encryption Homopai: A secure collaborative machine learning platform based on homomorphic encryption,

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:34:40.317738Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 9a144f14-ae19-4994-8911-e5c534fbb781 · outbound

This paper cites Copifl: A collusion-resistant and privacy-preserving federated learn- ing crowdsourcing scheme using blockchain and homomorphic en- cryption,.

Efficient Privacy-Preserving Cross-Silo Federated Learning with Multi-Key Homomorphic Encryption Copifl: A collusion-resistant and privacy-preserving federated learn- ing crowdsourcing scheme using blockchain and homomorphic en- cryption,

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:34:40.131657Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 4bc74865-a175-4d6c-8eb6-ae780621a7de · outbound

This paper cites Ho- momorphic encryption-based privacy-preserving federated learning in iot-enabled healthcare system,.

Efficient Privacy-Preserving Cross-Silo Federated Learning with Multi-Key Homomorphic Encryption Ho- momorphic encryption-based privacy-preserving federated learning in iot-enabled healthcare system,

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:34:39.939161Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 66957dfb-55f0-41e3-bede-1947641fc9be · outbound

This paper cites Secure aggregation in federated learning via multiparty homomorphic encryption,.

Efficient Privacy-Preserving Cross-Silo Federated Learning with Multi-Key Homomorphic Encryption Secure aggregation in federated learning via multiparty homomorphic encryption,

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:34:39.844548Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 13a9a622-2252-40d8-a127-f9d684c63f53 · outbound

This paper cites Dhsa: efficient doubly homomorphic secure aggregation for cross-silo federated learning,.

Efficient Privacy-Preserving Cross-Silo Federated Learning with Multi-Key Homomorphic Encryption Dhsa: efficient doubly homomorphic secure aggregation for cross-silo federated learning,

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:34:39.724333Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 72f36604-3a73-4c73-b1d4-9110009e5518 · outbound

This paper cites Fedmask: Joint computation and communication-efficient personalized feder- ated learning via heterogeneous masking,.

Efficient Privacy-Preserving Cross-Silo Federated Learning with Multi-Key Homomorphic Encryption Fedmask: Joint computation and communication-efficient personalized feder- ated learning via heterogeneous masking,

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:34:39.543446Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation b8cc638b-c931-4980-bde8-d43429904e32 · outbound

This paper cites Springer, 2010, pp.

Efficient Privacy-Preserving Cross-Silo Federated Learning with Multi-Key Homomorphic Encryption Springer, 2010, pp

Reference 2010

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:34:40.769896Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

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Reference resolution

Outbound references

Pith citing papers