Enabling Trustworthy Federated Learning via Remote Attestation for Mitigating Byzantine Threats | Paper Citation Record

Observation 651dd182-550f-4d56-ab46-4f5650c58b64 · outbound

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

Enabling Trustworthy Federated Learning via Remote Attestation for Mitigating Byzantine Threats Federated Learning: Strategies for Improving Communication Efficiency

Reference 1

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Observation dd1ada27-1ed2-43a7-9083-3b3222f34065 · outbound

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

Enabling Trustworthy Federated Learning via Remote Attestation for Mitigating Byzantine Threats Communication-efficient learning of deep networks from decentralized data,

Reference 2

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Observation a99d2c13-f5f1-4673-9fce-2183cd652218 · outbound

This paper cites Space–air–ground–sea integrated network with federated learning,.

Enabling Trustworthy Federated Learning via Remote Attestation for Mitigating Byzantine Threats Space–air–ground–sea integrated network with federated learning,

Reference 3

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Observation f464d4ed-f67a-4a4e-9f9a-e9c1d4d01f90 · outbound

This paper cites State-of-the-Art Approaches to Enhancing Privacy Preservation of Machine Learning Datasets: A Survey.

Enabling Trustworthy Federated Learning via Remote Attestation for Mitigating Byzantine Threats State-of-the-Art Approaches to Enhancing Privacy Preservation of Machine Learning Datasets: A Survey

Reference 4

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Observation bd1cf21a-e7b7-4865-8a58-de4f938d7cdf · outbound

This paper cites Federated learning for iout: Concepts, applications, challenges and future directions,.

Enabling Trustworthy Federated Learning via Remote Attestation for Mitigating Byzantine Threats Federated learning for iout: Concepts, applications, challenges and future directions,

Reference 5

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Observation eae10393-e41a-4bbc-aedb-ec71b1033c90 · outbound

This paper cites Mindfl: Mitigating the impact of imbalanced and noisy-labeled data in federated learning with quality and fairness-aware client selection,.

Enabling Trustworthy Federated Learning via Remote Attestation for Mitigating Byzantine Threats Mindfl: Mitigating the impact of imbalanced and noisy-labeled data in federated learning with quality and fairness-aware client selection,

Reference 6

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Observation 2c49674b-207c-4d5d-bcaa-ec13d3d2f473 · outbound

This paper cites an unresolved cited work.

Enabling Trustworthy Federated Learning via Remote Attestation for Mitigating Byzantine Threats Unresolved cited work

Reference 7

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Observation 07d2e03c-a04d-4722-9a4e-106dde29caf3 · outbound

This paper cites Feco: Boosting intrusion detection capability in iot networks via contrastive learning,.

Enabling Trustworthy Federated Learning via Remote Attestation for Mitigating Byzantine Threats Feco: Boosting intrusion detection capability in iot networks via contrastive learning,

Reference 8

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Observation 6cfb69b5-4c16-468a-9695-b29bbdea81cf · outbound

This paper cites Hermes: Boosting the performance of machine-learning-based intrusion detection system through geometric feature learning,.

Enabling Trustworthy Federated Learning via Remote Attestation for Mitigating Byzantine Threats Hermes: Boosting the performance of machine-learning-based intrusion detection system through geometric feature learning,

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T13:28:54.181517Z

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Observation 56423358-b9af-4589-b997-0d36fe54fde6 · outbound

This paper cites Machine learning- based intrusion detection systems: Capabilities, methodologies, and open research challenges,.

Enabling Trustworthy Federated Learning via Remote Attestation for Mitigating Byzantine Threats Machine learning- based intrusion detection systems: Capabilities, methodologies, and open research challenges,

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T13:28:53.957746Z

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Observation eb684207-1447-4b4a-bd74-a09ced81eb2e · outbound

This paper cites Free lunch for federated remote sensing target fine-grained classification: A parameter- efficient framework,.

Enabling Trustworthy Federated Learning via Remote Attestation for Mitigating Byzantine Threats Free lunch for federated remote sensing target fine-grained classification: A parameter- efficient framework,

Reference 11

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This paper cites StarCast: A Secure and Spectrum-Efficient Group Communication Scheme for LEO Satellite Networks,.

Enabling Trustworthy Federated Learning via Remote Attestation for Mitigating Byzantine Threats StarCast: A Secure and Spectrum-Efficient Group Communication Scheme for LEO Satellite Networks,

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T13:28:53.510815Z

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Observation d228b451-87fd-4f9f-81bb-996877c3584d · outbound

This paper cites Distributed statistical machine learning in adversarial settings: Byzantine gradient descent,.

Enabling Trustworthy Federated Learning via Remote Attestation for Mitigating Byzantine Threats Distributed statistical machine learning in adversarial settings: Byzantine gradient descent,

Reference 13

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Observation 9809cbc3-d110-4b4f-8951-5780bd96c055 · outbound

This paper cites Byzantine-resilient secure federated learning,.

Enabling Trustworthy Federated Learning via Remote Attestation for Mitigating Byzantine Threats Byzantine-resilient secure federated learning,

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T13:28:53.020133Z

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Observation 4ee45e5c-178f-4e65-82a4-bb74dba5c88f · outbound

This paper cites The hidden vulnerability of distributed learning in byzantium,.

Enabling Trustworthy Federated Learning via Remote Attestation for Mitigating Byzantine Threats The hidden vulnerability of distributed learning in byzantium,

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T13:28:52.776928Z

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Observation f9382dd2-bf62-4827-830f-e4032b38a116 · outbound

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

Enabling Trustworthy Federated Learning via Remote Attestation for Mitigating Byzantine Threats Local model poisoning attacks to byzantine-robust federated learning,

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T13:28:52.543225Z

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Observation ef19707b-5877-4eda-9ae2-87cd3aaf5da8 · outbound

This paper cites Analyzing feder- ated learning through an adversarial lens,.

Enabling Trustworthy Federated Learning via Remote Attestation for Mitigating Byzantine Threats Analyzing feder- ated learning through an adversarial lens,

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T13:28:52.379726Z

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Observation 70f76293-2a28-402b-8d79-5a0e1c3e195f · outbound

This paper cites Scale-mia: A scalable model inversion attack against secure federated learning via latent space reconstruction,.

Enabling Trustworthy Federated Learning via Remote Attestation for Mitigating Byzantine Threats Scale-mia: A scalable model inversion attack against secure federated learning via latent space reconstruction,

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T13:28:52.227856Z

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Observation 2274a343-b233-4fe6-8945-cd5e50cec124 · outbound

This paper cites MedLeak: Multimodal medical data leakage in secure federated learning with crafted models,.

Enabling Trustworthy Federated Learning via Remote Attestation for Mitigating Byzantine Threats MedLeak: Multimodal medical data leakage in secure federated learning with crafted models,

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T13:28:52.075373Z

Source-reported events for the cited work

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Observation 69f2ef7e-499f-4424-aca0-1af8d920f1ec · outbound

This paper cites Auror: Defending against poisoning attacks in collaborative deep learning systems,.

Enabling Trustworthy Federated Learning via Remote Attestation for Mitigating Byzantine Threats Auror: Defending against poisoning attacks in collaborative deep learning systems,

Reference 20

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Observation 56bbfcb8-492a-4c28-8c64-033f3923b16f · outbound

This paper cites Mitigating Sybils in Federated Learning Poisoning.

Enabling Trustworthy Federated Learning via Remote Attestation for Mitigating Byzantine Threats Mitigating Sybils in Federated Learning Poisoning

Reference 21

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Observation 7f37acc8-4b67-4fae-8c2c-f64b28fa6bd1 · outbound

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

Enabling Trustworthy Federated Learning via Remote Attestation for Mitigating Byzantine Threats Learning to Detect Malicious Clients for Robust Federated Learning

Reference 22

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Observation 0b879226-a73e-4cd5-8915-c43d7ca1831c · outbound

This paper cites Pdgan: A novel poisoning defense method in federated learning using generative adversarial network,.

Enabling Trustworthy Federated Learning via Remote Attestation for Mitigating Byzantine Threats Pdgan: A novel poisoning defense method in federated learning using generative adversarial network,

Reference 23

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This paper cites Deepsight: Mitigating backdoor attacks in federated learning through deep model inspection,.

Enabling Trustworthy Federated Learning via Remote Attestation for Mitigating Byzantine Threats Deepsight: Mitigating backdoor attacks in federated learning through deep model inspection,

Reference 24

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This paper cites Contra: Defending against poisoning attacks in federated learning,.

Enabling Trustworthy Federated Learning via Remote Attestation for Mitigating Byzantine Threats Contra: Defending against poisoning attacks in federated learning,

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T13:28:51.941641Z

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Observation d680c524-0b76-4f46-b843-e15719c980d9 · outbound

This paper cites Federated learning with hierarchical clustering of local updates to improve training on non-iid data,.

Enabling Trustworthy Federated Learning via Remote Attestation for Mitigating Byzantine Threats Federated learning with hierarchical clustering of local updates to improve training on non-iid data,

Reference 26

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Observation 3b2542d8-f20c-49d0-98f0-92b8c684727f · outbound

This paper cites flare: Defending federated learning against model poisoning attacks via latent space representations,.

Enabling Trustworthy Federated Learning via Remote Attestation for Mitigating Byzantine Threats flare: Defending federated learning against model poisoning attacks via latent space representations,

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T13:28:51.820367Z

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Observation cbdbe174-8ce0-4a97-946f-168563da35ca · outbound

This paper cites UCBlocker: Unwanted call blocking using anonymous authentication,.

Enabling Trustworthy Federated Learning via Remote Attestation for Mitigating Byzantine Threats UCBlocker: Unwanted call blocking using anonymous authentication,

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T13:28:51.683115Z

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This paper cites Aaka: An anti-tracking cellular authentication scheme leveraging anonymous credentials,.

Enabling Trustworthy Federated Learning via Remote Attestation for Mitigating Byzantine Threats Aaka: An anti-tracking cellular authentication scheme leveraging anonymous credentials,

Reference 29

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Observation eacb291e-492d-4b81-bee2-2fd590962ea7 · outbound

This paper cites Mobile tracking in 5g and beyond networks: Problems, challenges, and new directions,.

Enabling Trustworthy Federated Learning via Remote Attestation for Mitigating Byzantine Threats Mobile tracking in 5g and beyond networks: Problems, challenges, and new directions,

Reference 30

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This paper cites Bijack: Break- ing bitcoin network with tcp vulnerabilities,.

Enabling Trustworthy Federated Learning via Remote Attestation for Mitigating Byzantine Threats Bijack: Break- ing bitcoin network with tcp vulnerabilities,

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T13:28:51.591310Z

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Observation bb1129a1-ffaa-4608-8a20-03948d76bd3f · outbound

This paper cites Closing the visibility gap: A monitoring framework for verifiable open RAN operations,.

Enabling Trustworthy Federated Learning via Remote Attestation for Mitigating Byzantine Threats Closing the visibility gap: A monitoring framework for verifiable open RAN operations,

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T13:28:51.500807Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 500a1036-3565-4450-9fbe-2593dae82ac9 · outbound

This paper cites Deepattest: An end-to-end attestation framework for deep neural networks,.

Enabling Trustworthy Federated Learning via Remote Attestation for Mitigating Byzantine Threats Deepattest: An end-to-end attestation framework for deep neural networks,

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T13:28:51.369061Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 11135b73-4631-422c-be02-b5b729c0ad0f · outbound

This paper cites Ppfl: Privacy-preserving federated learning with trusted execution envi- ronments,.

Enabling Trustworthy Federated Learning via Remote Attestation for Mitigating Byzantine Threats Ppfl: Privacy-preserving federated learning with trusted execution envi- ronments,

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T13:28:51.218520Z

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Observation aced653b-e056-4106-8cdf-8138b3583d6b · outbound

This paper cites Sear: Secure and efficient aggregation for byzantine-robust federated learning,.

Enabling Trustworthy Federated Learning via Remote Attestation for Mitigating Byzantine Threats Sear: Secure and efficient aggregation for byzantine-robust federated learning,

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T13:28:51.063428Z

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Observation c5cf8c9b-c871-4ac4-85b1-deab4db70fd0 · outbound

This paper cites Intel sgx explained,.

Enabling Trustworthy Federated Learning via Remote Attestation for Mitigating Byzantine Threats Intel sgx explained,

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T13:28:50.894627Z

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Observation 60603543-131c-441d-ace0-840f29e92dce · outbound

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

Enabling Trustworthy Federated Learning via Remote Attestation for Mitigating Byzantine Threats Enabling execution assurance of federated learning at untrusted participants,

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T13:28:50.745860Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 9d9c1062-4c7b-44c3-ba24-2f18eaa068f4 · outbound

This paper cites A fpga-based heterogeneous implemen- tation of ntruencrypt,.

Enabling Trustworthy Federated Learning via Remote Attestation for Mitigating Byzantine Threats A fpga-based heterogeneous implemen- tation of ntruencrypt,

Reference 38

Resolution: unresolved; no resolver link, observed 2026-08-05T13:28:48.396931Z

Source-reported events for the cited work

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Observation 2865d09f-cd87-4ab3-8145-0cf8873ff978 · outbound

This paper cites High-performance and energy-efficient fpga-gpu-cpu heterogeneous system implementation,.

Enabling Trustworthy Federated Learning via Remote Attestation for Mitigating Byzantine Threats High-performance and energy-efficient fpga-gpu-cpu heterogeneous system implementation,

Reference 39

Resolution: unresolved; no resolver link, observed 2026-08-05T13:28:48.481037Z

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Observation ec52d37f-e11a-4e31-8602-9729a39ea640 · outbound

This paper cites Gpu acceleration of ciphertext-policy attribute-based encryption,.

Enabling Trustworthy Federated Learning via Remote Attestation for Mitigating Byzantine Threats Gpu acceleration of ciphertext-policy attribute-based encryption,

Reference 40

Resolution: unresolved; no resolver link, observed 2026-08-05T13:28:48.558364Z

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Observation 42600640-1fb8-48a1-91bb-9419a9543221 · outbound

This paper cites APEX: A verified architecture for proofs of execution on remote devices under full software compromise,.

Enabling Trustworthy Federated Learning via Remote Attestation for Mitigating Byzantine Threats APEX: A verified architecture for proofs of execution on remote devices under full software compromise,

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T13:28:50.588551Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 4d94160a-d201-4761-b6d1-5f323e4d029c · outbound

This paper cites Diat: Data integrity attestation for resilient collaboration of autonomous systems.,.

Enabling Trustworthy Federated Learning via Remote Attestation for Mitigating Byzantine Threats Diat: Data integrity attestation for resilient collaboration of autonomous systems.,

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T13:28:50.426746Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 72fb9b9b-63a0-4be6-aad5-3d03b85bc98f · outbound

This paper cites Oat: Attesting operation integrity of embedded devices,.

Enabling Trustworthy Federated Learning via Remote Attestation for Mitigating Byzantine Threats Oat: Attesting operation integrity of embedded devices,

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T13:28:50.288852Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation bd79520a-5f0f-4aea-8c06-26819f8159e9 · outbound

This paper cites {ARI}: Attestation of real-time mission execution integrity,.

Enabling Trustworthy Federated Learning via Remote Attestation for Mitigating Byzantine Threats {ARI}: Attestation of real-time mission execution integrity,

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T13:28:50.111833Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 4f6edcde-b199-4079-ba3d-c5fbc81ceef8 · outbound

This paper cites {ACES}: Automatic compartments for embedded systems,.

Enabling Trustworthy Federated Learning via Remote Attestation for Mitigating Byzantine Threats {ACES}: Automatic compartments for embedded systems,

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T13:28:49.950335Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 75ec0a36-f6b8-4f97-a0a1-6a1a31caf1df · outbound

This paper cites Trustzone: Integrated hardware and software security,.

Enabling Trustworthy Federated Learning via Remote Attestation for Mitigating Byzantine Threats Trustzone: Integrated hardware and software security,

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T13:28:49.838394Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation af9c96f3-8fa2-4242-a6d2-3f105dcc135d · outbound

This paper cites C-flat: control-flow attestation for embedded systems software,.

Enabling Trustworthy Federated Learning via Remote Attestation for Mitigating Byzantine Threats C-flat: control-flow attestation for embedded systems software,

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T13:28:49.713361Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 3f4f7639-e4a4-4da9-aef1-009b3472bdd1 · outbound

This paper cites Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms.

Enabling Trustworthy Federated Learning via Remote Attestation for Mitigating Byzantine Threats Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms

Reference 48

Resolution: unresolved; no resolver link, observed 2026-08-05T13:28:49.158583Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0850c8c6-cf65-4f62-bd86-1ce7a5b8265e · outbound

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

Enabling Trustworthy Federated Learning via Remote Attestation for Mitigating Byzantine Threats Learning multiple layers of features from tiny images,

Reference 49

Resolution: unresolved; no resolver link, observed 2026-08-05T13:28:49.236279Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3be8edcf-2a1a-4d9a-a68f-9be18da5292b · outbound

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

Enabling Trustworthy Federated Learning via Remote Attestation for Mitigating Byzantine Threats Machine learning with adversaries: Byzantine tolerant gradient descent,

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T13:28:49.588287Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation b93fe95c-d18f-4753-9f4d-62a32db025c8 · outbound

This paper cites Byzantine-Robust Distributed Learning: Towards Optimal Statistical Rates.

Enabling Trustworthy Federated Learning via Remote Attestation for Mitigating Byzantine Threats Byzantine-Robust Distributed Learning: Towards Optimal Statistical Rates

Reference 51

Resolution: unresolved; no resolver link, observed 2026-08-05T13:28:49.338280Z

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Observation 004d4343-88fe-4e6f-b6e0-b9950d832f3d · outbound

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

Enabling Trustworthy Federated Learning via Remote Attestation for Mitigating Byzantine Threats Fltrust: Byzantine-robust federated learning via trust bootstrapping,

Reference 52

Resolution: unresolved; no resolver link, observed 2026-08-05T13:28:49.396226Z

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