FL-PLAS: Federated Learning with Partial Layer Aggregation for Backdoor Defense Against High-Ratio Malicious Clients | Paper Citation Record

Observation 628a6754-36c9-463d-ad77-b9f32a785887 · outbound

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

FL-PLAS: Federated Learning with Partial Layer Aggregation for Backdoor Defense Against High-Ratio Malicious Clients Federated Learning: Strategies for Improving Communication Efficiency

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-15T20:46:59.156571Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b5fc46fe-38a7-410b-bfbe-a04d8ca3d162 · outbound

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

FL-PLAS: Federated Learning with Partial Layer Aggregation for Backdoor Defense Against High-Ratio Malicious Clients Communication- efficient learning of deep networks from decentralized data

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:47:00.439682Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation a7c53513-f035-41f3-9c53-fe64eab88e78 · outbound

This paper cites Vulnerabilities in federated learning.

FL-PLAS: Federated Learning with Partial Layer Aggregation for Backdoor Defense Against High-Ratio Malicious Clients Vulnerabilities in federated learning

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:47:00.426555Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 216bdf9b-6d02-47a2-9e3b-e3b7a4cd4cfc · outbound

This paper cites Defending against backdoors in federated learning with robust learning rate.

FL-PLAS: Federated Learning with Partial Layer Aggregation for Backdoor Defense Against High-Ratio Malicious Clients Defending against backdoors in federated learning with robust learning rate

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:47:00.414155Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 4052a6b0-f4ab-4edd-8209-07c3f7fb0395 · outbound

This paper cites Targeted Backdoor Attacks on Deep Learning Systems Using Data Poisoning.

FL-PLAS: Federated Learning with Partial Layer Aggregation for Backdoor Defense Against High-Ratio Malicious Clients Targeted Backdoor Attacks on Deep Learning Systems Using Data Poisoning

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-15T20:46:59.174973Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0c12bd55-f91a-4f47-8e07-df0e95e65c6a · outbound

This paper cites BadNets: Identifying Vulnerabilities in the Machine Learning Model Supply Chain.

FL-PLAS: Federated Learning with Partial Layer Aggregation for Backdoor Defense Against High-Ratio Malicious Clients BadNets: Identifying Vulnerabilities in the Machine Learning Model Supply Chain

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-15T20:46:59.179416Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e9e0ea98-9d14-41b9-867d-df94004f5ba1 · outbound

This paper cites How to backdoor federated learning.

FL-PLAS: Federated Learning with Partial Layer Aggregation for Backdoor Defense Against High-Ratio Malicious Clients How to backdoor federated learning

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:47:00.401443Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation fbec6874-2126-4fca-90d7-42eac3d30eae · outbound

This paper cites Data poisoning attacks against federated learning systems.

FL-PLAS: Federated Learning with Partial Layer Aggregation for Backdoor Defense Against High-Ratio Malicious Clients Data poisoning attacks against federated learning systems

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:47:00.388565Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 4ddc3bfd-161d-49dc-898b-14cba833af26 · outbound

This paper cites Lfighter: Defend- ing against the label-flipping attack in federated learning.

FL-PLAS: Federated Learning with Partial Layer Aggregation for Backdoor Defense Against High-Ratio Malicious Clients Lfighter: Defend- ing against the label-flipping attack in federated learning

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:47:00.374034Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 9d9f491b-2da7-4b4d-8870-9d9ce40fe085 · outbound

This paper cites Attack of the Tails: Yes, You Really Can Backdoor Federated Learning.

FL-PLAS: Federated Learning with Partial Layer Aggregation for Backdoor Defense Against High-Ratio Malicious Clients Attack of the Tails: Yes, You Really Can Backdoor Federated Learning

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-15T20:46:59.196443Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2eae8392-1c94-491d-bcf9-410386682d16 · outbound

This paper cites On the vulnerability of backdoor defenses for federated learning.

FL-PLAS: Federated Learning with Partial Layer Aggregation for Backdoor Defense Against High-Ratio Malicious Clients On the vulnerability of backdoor defenses for federated learning

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:47:00.361357Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation dade1b77-8672-43fc-a441-6e3797517e6a · outbound

This paper cites Backdoor federated learning by poisoning backdoor-critical layers.

FL-PLAS: Federated Learning with Partial Layer Aggregation for Backdoor Defense Against High-Ratio Malicious Clients Backdoor federated learning by poisoning backdoor-critical layers

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:47:00.349004Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 260aae2f-dcd6-49cd-8d80-cee6be1c9eb0 · outbound

This paper cites Threats to Federated Learning: A Survey.

FL-PLAS: Federated Learning with Partial Layer Aggregation for Backdoor Defense Against High-Ratio Malicious Clients Threats to Federated Learning: A Survey

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-15T20:46:59.208880Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4196438b-f561-404c-a943-45fdf616d445 · outbound

This paper cites Giannakis, and Qing Ling.

FL-PLAS: Federated Learning with Partial Layer Aggregation for Backdoor Defense Against High-Ratio Malicious Clients Giannakis, and Qing Ling

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:47:00.335692Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 2746a314-7624-4552-bad3-e67725cc083a · outbound

This paper cites Can You Really Backdoor Federated Learning?.

FL-PLAS: Federated Learning with Partial Layer Aggregation for Backdoor Defense Against High-Ratio Malicious Clients Can You Really Backdoor Federated Learning?

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-15T20:46:59.216845Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 662c3bd4-b52a-457d-945c-3521099037d5 · outbound

This paper cites Learning to Detect Malicious Clients for Robust Federated Learning.

FL-PLAS: Federated Learning with Partial Layer Aggregation for Backdoor Defense Against High-Ratio Malicious Clients Learning to Detect Malicious Clients for Robust Federated Learning

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-15T20:46:59.220971Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6b06f5ce-e3dd-47df-a078-02afdb49dd51 · outbound

This paper cites Fltrust: Byzantine-robust federated learning via trust bootstrapping.

FL-PLAS: Federated Learning with Partial Layer Aggregation for Backdoor Defense Against High-Ratio Malicious Clients Fltrust: Byzantine-robust federated learning via trust bootstrapping

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:47:00.322445Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 565313d2-c4e7-4185-bba9-2b9df3d1d625 · outbound

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

FL-PLAS: Federated Learning with Partial Layer Aggregation for Backdoor Defense Against High-Ratio Malicious Clients {FLAME}: Taming backdoors in federated learning

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-15T20:46:59.228856Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c5369117-0988-46bb-bd38-05b19bf38ed1 · outbound

This paper cites Machine learning with adversaries: Byzantine tolerant gradient descent.

FL-PLAS: Federated Learning with Partial Layer Aggregation for Backdoor Defense Against High-Ratio Malicious Clients Machine learning with adversaries: Byzantine tolerant gradient descent

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:47:00.301147Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 6a4bb6a5-936a-4190-9e4f-58049b6fcc5e · outbound

This paper cites Backdooring convolutional neural networks via targeted weight perturbations.

FL-PLAS: Federated Learning with Partial Layer Aggregation for Backdoor Defense Against High-Ratio Malicious Clients Backdooring convolutional neural networks via targeted weight perturbations

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:47:00.289217Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 2d548116-c73f-46d5-a0c7-fb8ea56fd084 · outbound

This paper cites Data poisoning attacks and defenses to crowdsourcing systems.

FL-PLAS: Federated Learning with Partial Layer Aggregation for Backdoor Defense Against High-Ratio Malicious Clients Data poisoning attacks and defenses to crowdsourcing systems

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:47:00.277203Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 36027c5c-3a86-429e-9b07-3093c7d9c391 · outbound

This paper cites Poisoning attacks to graph-based recommender systems.

FL-PLAS: Federated Learning with Partial Layer Aggregation for Backdoor Defense Against High-Ratio Malicious Clients Poisoning attacks to graph-based recommender systems

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:47:00.265119Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 3ef81319-0178-4ed5-bc1c-286135ef027f · outbound

This paper cites Fake co-visitation injection attacks to recommender systems.

FL-PLAS: Federated Learning with Partial Layer Aggregation for Backdoor Defense Against High-Ratio Malicious Clients Fake co-visitation injection attacks to recommender systems

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:47:00.253075Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 7f89b187-bcfe-4598-a64b-5a3d30cb21da · outbound

This paper cites Exploiting machine learning to subvert your spam filter.

FL-PLAS: Federated Learning with Partial Layer Aggregation for Backdoor Defense Against High-Ratio Malicious Clients Exploiting machine learning to subvert your spam filter

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:47:00.241011Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation db390e47-3344-4f1f-9c2f-7316b56109ec · outbound

This paper cites Advances and Open Problems in Federated Learning.

FL-PLAS: Federated Learning with Partial Layer Aggregation for Backdoor Defense Against High-Ratio Malicious Clients Advances and Open Problems in Federated Learning

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-15T20:46:59.256703Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 09b0fdbc-8bed-4fe0-a594-eadbf2ef6740 · outbound

This paper cites Backdoor Learning: A Survey.

FL-PLAS: Federated Learning with Partial Layer Aggregation for Backdoor Defense Against High-Ratio Malicious Clients Backdoor Learning: A Survey

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-15T20:46:59.260987Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 49e3a2a1-fe5f-4cba-b243-b19c2834a517 · outbound

This paper cites Byzantine-Robust Learning on Heterogeneous Datasets via Bucketing.

FL-PLAS: Federated Learning with Partial Layer Aggregation for Backdoor Defense Against High-Ratio Malicious Clients Byzantine-Robust Learning on Heterogeneous Datasets via Bucketing

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-15T20:46:59.265312Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5ba3842a-91b0-45cd-a1dc-fe8b9c207107 · outbound

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

FL-PLAS: Federated Learning with Partial Layer Aggregation for Backdoor Defense Against High-Ratio Malicious Clients Measuring the Effects of Non-Identical Data Distribution for Federated Visual Classification

Reference 28

Resolution: unresolved; no resolver link, observed 2026-08-15T20:46:59.269254Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 617e94f5-6b4e-4a2e-9c58-6f38c4f525f2 · outbound

This paper cites Explaining and Harnessing Adversarial Examples.

FL-PLAS: Federated Learning with Partial Layer Aggregation for Backdoor Defense Against High-Ratio Malicious Clients Explaining and Harnessing Adversarial Examples

Reference 29

Resolution: unresolved; no resolver link, observed 2026-08-15T20:46:59.273346Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4661f1e9-3062-4157-9b47-e080f3c68835 · outbound

This paper cites The limitations of deep learning in adversarial settings.

FL-PLAS: Federated Learning with Partial Layer Aggregation for Backdoor Defense Against High-Ratio Malicious Clients The limitations of deep learning in adversarial settings

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:47:00.228445Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation e86f0cbb-4fbd-4371-8ec3-7f0d914b16ec · outbound

This paper cites Terminal brain damage: Exposing the graceless degradation in deep neural networks under hardware fault attacks.

FL-PLAS: Federated Learning with Partial Layer Aggregation for Backdoor Defense Against High-Ratio Malicious Clients Terminal brain damage: Exposing the graceless degradation in deep neural networks under hardware fault attacks

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:47:00.216304Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 833223c5-bc26-427b-8ed4-f0abf23a68ad · outbound

This paper cites Antidote: understanding and defending against poisoning of anomaly detectors.

FL-PLAS: Federated Learning with Partial Layer Aggregation for Backdoor Defense Against High-Ratio Malicious Clients Antidote: understanding and defending against poisoning of anomaly detectors

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:47:00.203431Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation f06ecca4-5bb3-4c13-8991-59e893255fcf · outbound

This paper cites Ronny Huang, Mahyar Najibi, Octavian Suciu, Christoph Studer, Tudor Dumitras, and Tom Goldstein.

FL-PLAS: Federated Learning with Partial Layer Aggregation for Backdoor Defense Against High-Ratio Malicious Clients Ronny Huang, Mahyar Najibi, Octavian Suciu, Christoph Studer, Tudor Dumitras, and Tom Goldstein

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:47:00.191092Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation e1e9185e-806b-4cb6-9984-8e0cb95ec25a · outbound

This paper cites When does machine learning fail? generalized transferability for evasion and poisoning attacks.

FL-PLAS: Federated Learning with Partial Layer Aggregation for Backdoor Defense Against High-Ratio Malicious Clients When does machine learning fail? generalized transferability for evasion and poisoning attacks

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:47:00.178724Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation b7acef71-fe2c-4089-ad0c-d1dcf90ffb97 · outbound

This paper cites Attacking graph-based classification via manipulating the graph structure.

FL-PLAS: Federated Learning with Partial Layer Aggregation for Backdoor Defense Against High-Ratio Malicious Clients Attacking graph-based classification via manipulating the graph structure

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:47:00.166317Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 6daa4b75-5f16-4387-bc81-07496230f474 · outbound

This paper cites Poisoning attacks against support vector machines.

FL-PLAS: Federated Learning with Partial Layer Aggregation for Backdoor Defense Against High-Ratio Malicious Clients Poisoning attacks against support vector machines

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:47:00.153332Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation d1b532bb-8943-4f22-9406-f239ff93afcf · outbound

This paper cites Manipulating machine learning: Poisoning attacks and countermeasures for regression learning.

FL-PLAS: Federated Learning with Partial Layer Aggregation for Backdoor Defense Against High-Ratio Malicious Clients Manipulating machine learning: Poisoning attacks and countermeasures for regression learning

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:47:00.140464Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation d18f68a5-685c-49bb-9f58-ca0aaa8b8a25 · outbound

This paper cites Data poisoning attacks on factorization-based collaborative filtering.

FL-PLAS: Federated Learning with Partial Layer Aggregation for Backdoor Defense Against High-Ratio Malicious Clients Data poisoning attacks on factorization-based collaborative filtering

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:47:00.127861Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 142cabd6-0b68-4844-8c97-c63a7039adf5 · outbound

This paper cites Towards poisoning of deep learning algorithms with back-gradient optimization.

FL-PLAS: Federated Learning with Partial Layer Aggregation for Backdoor Defense Against High-Ratio Malicious Clients Towards poisoning of deep learning algorithms with back-gradient optimization

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:47:00.115688Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 6f8c30dc-8714-4409-9ea0-dde061011096 · outbound

This paper cites Is feature selection secure against training data poisoning? In Proceedings of the 32nd International Conference on Machine Learning, ICML, volume 37, pages 1689–1698.

FL-PLAS: Federated Learning with Partial Layer Aggregation for Backdoor Defense Against High-Ratio Malicious Clients Is feature selection secure against training data poisoning? In Proceedings of the 32nd International Conference on Machine Learning, ICML, volume 37, pages 1689–1698

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:47:00.102137Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation fe4eb3e5-7e89-45b0-af6a-121d3c95d244 · outbound

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

FL-PLAS: Federated Learning with Partial Layer Aggregation for Backdoor Defense Against High-Ratio Malicious Clients Local model poisoning attacks to byzantine-robust federated learning

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:47:00.087864Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation f265cbf2-012f-40a8-ae4f-fe6b85fdfa73 · outbound

This paper cites A little is enough: Circumventing defenses for distributed learning.

FL-PLAS: Federated Learning with Partial Layer Aggregation for Backdoor Defense Against High-Ratio Malicious Clients A little is enough: Circumventing defenses for distributed learning

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:47:00.074117Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 309ed70f-4848-471f-a6ec-3ffb7d624be6 · outbound

This paper cites Fall of empires: Breaking byzantine-tolerant SGD by inner product manipulation.

FL-PLAS: Federated Learning with Partial Layer Aggregation for Backdoor Defense Against High-Ratio Malicious Clients Fall of empires: Breaking byzantine-tolerant SGD by inner product manipulation

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:47:00.060188Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 81c479bb-13c7-4f8f-8b22-d567ff6fc807 · outbound

This paper cites an unresolved cited work.

FL-PLAS: Federated Learning with Partial Layer Aggregation for Backdoor Defense Against High-Ratio Malicious Clients Unresolved cited work

Reference 44

Resolution: unresolved; raw_fallback, observed 2026-08-15T20:47:00.046525Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 0d2b1d81-8598-4456-9bb4-1b406c19a06e · outbound

This paper cites DBA: distributed backdoor attacks against federated learning.

FL-PLAS: Federated Learning with Partial Layer Aggregation for Backdoor Defense Against High-Ratio Malicious Clients DBA: distributed backdoor attacks against federated learning

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:47:00.033408Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 036fb7cc-c657-4fe4-b2f9-a5467a6c16c3 · outbound

This paper cites Neural trojans.

FL-PLAS: Federated Learning with Partial Layer Aggregation for Backdoor Defense Against High-Ratio Malicious Clients Neural trojans

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:47:00.011019Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation e87ae1b7-a21d-4f0a-8a02-b5048c90c923 · outbound

This paper cites Februus: Input purification defense against trojan attacks on deep neural network systems.

FL-PLAS: Federated Learning with Partial Layer Aggregation for Backdoor Defense Against High-Ratio Malicious Clients Februus: Input purification defense against trojan attacks on deep neural network systems

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:46:59.998037Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation dc98f9be-8ffb-4570-879a-9fa17a5f3bf4 · outbound

This paper cites Bridging Mode Connectivity in Loss Landscapes and Adversarial Robustness.

FL-PLAS: Federated Learning with Partial Layer Aggregation for Backdoor Defense Against High-Ratio Malicious Clients Bridging Mode Connectivity in Loss Landscapes and Adversarial Robustness

Reference 48

Resolution: unresolved; no resolver link, observed 2026-08-15T20:46:59.352317Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e566a417-08e2-4d4a-964b-07ae8c25ec8e · outbound

This paper cites Fine-pruning: Defending against backdooring attacks on deep neural networks.

FL-PLAS: Federated Learning with Partial Layer Aggregation for Backdoor Defense Against High-Ratio Malicious Clients Fine-pruning: Defending against backdooring attacks on deep neural networks

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:46:59.984988Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 990d3f4d-da1c-4dc7-8e0e-05dbb72786a8 · outbound

This paper cites Spectral signatures in backdoor attacks.

FL-PLAS: Federated Learning with Partial Layer Aggregation for Backdoor Defense Against High-Ratio Malicious Clients Spectral signatures in backdoor attacks

Reference 50

Resolution: unresolved; no resolver link, observed 2026-08-15T20:46:59.360704Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5124a28d-afe1-4923-be23-4de00daf5b1e · outbound

This paper cites Detecting Backdoor Attacks on Deep Neural Networks by Activation Clustering.

FL-PLAS: Federated Learning with Partial Layer Aggregation for Backdoor Defense Against High-Ratio Malicious Clients Detecting Backdoor Attacks on Deep Neural Networks by Activation Clustering

Reference 51

Resolution: unresolved; no resolver link, observed 2026-08-15T20:46:59.364517Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation fcb72861-4f83-41ed-9f94-ba82f75f31a0 · outbound

This paper cites Demon in the variant: Statistical analysis of dnns for robust backdoor contamination detection.

FL-PLAS: Federated Learning with Partial Layer Aggregation for Backdoor Defense Against High-Ratio Malicious Clients Demon in the variant: Statistical analysis of dnns for robust backdoor contamination detection

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:46:59.961728Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation c824a161-dca0-4a87-8da8-cfd0aaf90670 · outbound

This paper cites Baffle: Backdoor detection via feedback-based federated learning.

FL-PLAS: Federated Learning with Partial Layer Aggregation for Backdoor Defense Against High-Ratio Malicious Clients Baffle: Backdoor detection via feedback-based federated learning

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:46:59.948265Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 9838a3bf-f74e-4657-bfbd-1aabe0c0596a · outbound

This paper cites Strip: A defence against trojan attacks on deep neural networks.

FL-PLAS: Federated Learning with Partial Layer Aggregation for Backdoor Defense Against High-Ratio Malicious Clients Strip: A defence against trojan attacks on deep neural networks

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:46:59.934682Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation d8220cac-d5a3-4506-af0a-702516a1adb9 · outbound

This paper cites Deep Probabilistic Models to Detect Data Poisoning Attacks.

FL-PLAS: Federated Learning with Partial Layer Aggregation for Backdoor Defense Against High-Ratio Malicious Clients Deep Probabilistic Models to Detect Data Poisoning Attacks

Reference 55

Resolution: unresolved; no resolver link, observed 2026-08-15T20:46:59.380682Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 14f0d770-92c4-4776-a143-e0aa6d0c6f07 · outbound

This paper cites Can We Mitigate Backdoor Attack Using Adversarial Detection Methods?.

FL-PLAS: Federated Learning with Partial Layer Aggregation for Backdoor Defense Against High-Ratio Malicious Clients Can We Mitigate Backdoor Attack Using Adversarial Detection Methods?

Reference 56

Resolution: verified exact; local_arxiv, observed 2026-08-15T20:46:59.568759Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 48974221-f73c-440c-bc26-b80ec2d2ad8b · outbound

This paper cites Fedinv: Byzantine-robust federated learning by inversing local model updates.

FL-PLAS: Federated Learning with Partial Layer Aggregation for Backdoor Defense Against High-Ratio Malicious Clients Fedinv: Byzantine-robust federated learning by inversing local model updates

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:46:59.921419Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation f1c94e5c-40cc-4d36-a06c-c4d3f45fdbb4 · outbound

This paper cites Flip: A provable defense framework for backdoor mitigation in federated learning.

FL-PLAS: Federated Learning with Partial Layer Aggregation for Backdoor Defense Against High-Ratio Malicious Clients Flip: A provable defense framework for backdoor mitigation in federated learning

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:46:59.908172Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 086b95fe-533d-422f-bc1b-0d6e28ef4057 · outbound

This paper cites Using Anomaly Detection to Detect Poisoning Attacks in Federated Learning Applications.

FL-PLAS: Federated Learning with Partial Layer Aggregation for Backdoor Defense Against High-Ratio Malicious Clients Using Anomaly Detection to Detect Poisoning Attacks in Federated Learning Applications

Reference 59

Resolution: unresolved; no resolver link, observed 2026-08-15T20:46:59.396695Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e511639d-2ca9-4659-bcc9-a58fe15599b7 · outbound

This paper cites Exploiting shared representations for personalized federated learning.

FL-PLAS: Federated Learning with Partial Layer Aggregation for Backdoor Defense Against High-Ratio Malicious Clients Exploiting shared representations for personalized federated learning

Reference 60

Resolution: unresolved; no resolver link, observed 2026-08-15T20:46:59.400671Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 016e92c2-d0ae-4037-885a-1cac6b60d188 · outbound

This paper cites Efficient wireless federated learning with partial model aggregation.

FL-PLAS: Federated Learning with Partial Layer Aggregation for Backdoor Defense Against High-Ratio Malicious Clients Efficient wireless federated learning with partial model aggregation

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:46:59.886035Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation e91b606f-d71a-4b48-b00d-b47466865502 · outbound

This paper cites Federated learning with partial model personalization.

FL-PLAS: Federated Learning with Partial Layer Aggregation for Backdoor Defense Against High-Ratio Malicious Clients Federated learning with partial model personalization

Reference 62

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:46:59.872782Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 23e5d9e7-71f3-40a6-8e3b-ee390cd83685 · outbound

This paper cites Federated Learning with Personalization Layers.

FL-PLAS: Federated Learning with Partial Layer Aggregation for Backdoor Defense Against High-Ratio Malicious Clients Federated Learning with Personalization Layers

Reference 63

Resolution: unresolved; no resolver link, observed 2026-08-15T20:46:59.412628Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1c39584a-327c-4c48-b80e-25d92e8d9edd · outbound

This paper cites Personalized federated learning with moreau envelopes.

FL-PLAS: Federated Learning with Partial Layer Aggregation for Backdoor Defense Against High-Ratio Malicious Clients Personalized federated learning with moreau envelopes

Reference 64

Resolution: unresolved; no resolver link, observed 2026-08-15T20:46:59.416762Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 15ded010-e57d-4a7e-b4c1-775e26c7b163 · outbound

This paper cites pFedSim: Similarity-Aware Model Aggregation Towards Personalized Federated Learning.

FL-PLAS: Federated Learning with Partial Layer Aggregation for Backdoor Defense Against High-Ratio Malicious Clients pFedSim: Similarity-Aware Model Aggregation Towards Personalized Federated Learning

Reference 65

Resolution: unresolved; no resolver link, observed 2026-08-15T20:46:59.421002Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9ad22181-4d3f-49bc-a2d8-32d1f2310f33 · outbound

This paper cites End-to-End Evaluation of Federated Learning and Split Learning for Internet of Things.

FL-PLAS: Federated Learning with Partial Layer Aggregation for Backdoor Defense Against High-Ratio Malicious Clients End-to-End Evaluation of Federated Learning and Split Learning for Internet of Things

Reference 66

Resolution: unresolved; no resolver link, observed 2026-08-15T20:46:59.426048Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5862e7e1-643e-4777-943d-f42ecee13668 · outbound

This paper cites Revisiting personalized federated learning: Robustness against backdoor attacks.

FL-PLAS: Federated Learning with Partial Layer Aggregation for Backdoor Defense Against High-Ratio Malicious Clients Revisiting personalized federated learning: Robustness against backdoor attacks

Reference 67

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:46:59.851565Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation a5f98215-0422-4f2c-ab36-05348519d429 · outbound

This paper cites One-pixel signature: Characterizing cnn models for backdoor detection.

FL-PLAS: Federated Learning with Partial Layer Aggregation for Backdoor Defense Against High-Ratio Malicious Clients One-pixel signature: Characterizing cnn models for backdoor detection

Reference 68

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:46:59.838472Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation ffab6e22-76f6-4010-ad1e-5707eb8bf66e · outbound

This paper cites Xmam: X-raying models with a matrix to reveal backdoor attacks for federated learning.

FL-PLAS: Federated Learning with Partial Layer Aggregation for Backdoor Defense Against High-Ratio Malicious Clients Xmam: X-raying models with a matrix to reveal backdoor attacks for federated learning

Reference 69

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:46:59.825320Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation ef2fc77b-f403-4145-b8cb-05c2475924c9 · outbound

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

FL-PLAS: Federated Learning with Partial Layer Aggregation for Backdoor Defense Against High-Ratio Malicious Clients Gradient-based learning applied to document recognition

Reference 70

Resolution: unresolved; no resolver link, observed 2026-08-15T20:46:59.442331Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e563ed44-6932-4fd1-b7ee-741d365dc6fa · outbound

This paper cites Handwritten digit recognition with a back-propagation network.Advances in neural information processing systems, 2, 1989.

FL-PLAS: Federated Learning with Partial Layer Aggregation for Backdoor Defense Against High-Ratio Malicious Clients Handwritten digit recognition with a back-propagation network.Advances in neural information processing systems, 2, 1989

Reference 71

Resolution: unresolved; no resolver link, observed 2026-08-15T20:46:59.446310Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation bfeb4a41-137c-4832-b623-8405364a9f31 · outbound

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

FL-PLAS: Federated Learning with Partial Layer Aggregation for Backdoor Defense Against High-Ratio Malicious Clients Learning multiple layers of features from tiny images

Reference 72

Resolution: unresolved; no resolver link, observed 2026-08-15T20:46:59.450356Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7b8b65b9-1b10-4f71-a55e-57d8ba8063d4 · outbound

This paper cites Deep residual learning for image recognition.

FL-PLAS: Federated Learning with Partial Layer Aggregation for Backdoor Defense Against High-Ratio Malicious Clients Deep residual learning for image recognition

Reference 73

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:46:59.788315Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 175686b9-f703-431b-a961-57f0b1bc55fa · outbound

This paper cites MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications.

FL-PLAS: Federated Learning with Partial Layer Aggregation for Backdoor Defense Against High-Ratio Malicious Clients MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications

Reference 74

Resolution: unresolved; no resolver link, observed 2026-08-15T20:46:59.458791Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ee5caf8f-5c8d-41a7-b001-9616608e23bb · outbound

This paper cites Byzantine-robust distributed learning: Towards optimal statistical rates.

FL-PLAS: Federated Learning with Partial Layer Aggregation for Backdoor Defense Against High-Ratio Malicious Clients Byzantine-robust distributed learning: Towards optimal statistical rates

Reference 75

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:46:59.775069Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 69c3a31f-1aeb-44c6-a488-d22bcb7dd5ea · outbound

This paper cites an unresolved cited work.

FL-PLAS: Federated Learning with Partial Layer Aggregation for Backdoor Defense Against High-Ratio Malicious Clients Unresolved cited work

Reference 2020

Resolution: parse uncertain; no resolver link, observed 2026-08-15T20:46:59.340558Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Coverage vector

Reference resolution

Outbound references

Pith citing papers