A Robust Federated Learning Framework for Undependable Devices at Scale | Paper Citation Record

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This paper cites Deep learning.

A Robust Federated Learning Framework for Undependable Devices at Scale Deep learning

Reference 1

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This paper cites Natu- ral language processing: state of the art, current trends and challenges.

A Robust Federated Learning Framework for Undependable Devices at Scale Natu- ral language processing: state of the art, current trends and challenges

Reference 2

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This paper cites Dif- ferentially private image classification by learning priors from random processes.

A Robust Federated Learning Framework for Undependable Devices at Scale Dif- ferentially private image classification by learning priors from random processes

Reference 3

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This paper cites Federated learning for generalization, robustness, fairness: A survey and benchmark.

A Robust Federated Learning Framework for Undependable Devices at Scale Federated learning for generalization, robustness, fairness: A survey and benchmark

Reference 4

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This paper cites Vertical federated learning: Concepts, advances, and challenges.

A Robust Federated Learning Framework for Undependable Devices at Scale Vertical federated learning: Concepts, advances, and challenges

Reference 5

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This paper cites Billion-scale federated learning on mobile clients: A submodel design with tunable privacy.

A Robust Federated Learning Framework for Undependable Devices at Scale Billion-scale federated learning on mobile clients: A submodel design with tunable privacy

Reference 6

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This paper cites Applications of federated learning in smart cities: recent advances, taxonomy, and open challenges.

A Robust Federated Learning Framework for Undependable Devices at Scale Applications of federated learning in smart cities: recent advances, taxonomy, and open challenges

Reference 7

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This paper cites Federated learning for smart healthcare: A survey.

A Robust Federated Learning Framework for Undependable Devices at Scale Federated learning for smart healthcare: A survey

Reference 8

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This paper cites Towards federated learning at scale: System design.

A Robust Federated Learning Framework for Undependable Devices at Scale Towards federated learning at scale: System design

Reference 9

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

A Robust Federated Learning Framework for Undependable Devices at Scale Communication-efficient learning of deep networks from decentralized data

Reference 10

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This paper cites Safa: A semi-asynchronous protocol for fast federated learning with low overhead.IEEE Transactions on Computers, 70(5):655– 668, 2020.

A Robust Federated Learning Framework for Undependable Devices at Scale Safa: A semi-asynchronous protocol for fast federated learning with low overhead.IEEE Transactions on Computers, 70(5):655– 668, 2020

Reference 11

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This paper cites Oort: Efficient federated learning via guided participant selection.

A Robust Federated Learning Framework for Undependable Devices at Scale Oort: Efficient federated learning via guided participant selection

Reference 12

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This paper cites Hermes: an efficient federated learning framework for heterogeneous mobile clients.

A Robust Federated Learning Framework for Undependable Devices at Scale Hermes: an efficient federated learning framework for heterogeneous mobile clients

Reference 13

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This paper cites Pyramidfl: A fine-grained client selection framework for efficient federated learning.

A Robust Federated Learning Framework for Undependable Devices at Scale Pyramidfl: A fine-grained client selection framework for efficient federated learning

Reference 14

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This paper cites Fedsea: A semi-asynchronous federated learning framework for extremely heterogeneous devices.

A Robust Federated Learning Framework for Undependable Devices at Scale Fedsea: A semi-asynchronous federated learning framework for extremely heterogeneous devices

Reference 15

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This paper cites AsyncFedED: Asynchronous Federated Learning with Euclidean Distance based Adaptive Weight Aggregation.

A Robust Federated Learning Framework for Undependable Devices at Scale AsyncFedED: Asynchronous Federated Learning with Euclidean Distance based Adaptive Weight Aggregation

Reference 16

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This paper cites Bose: Block-wise federated learning in het- erogeneous edge computing.

A Robust Federated Learning Framework for Undependable Devices at Scale Bose: Block-wise federated learning in het- erogeneous edge computing

Reference 17

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This paper cites Efficient federated learning for modern nlp.

A Robust Federated Learning Framework for Undependable Devices at Scale Efficient federated learning for modern nlp

Reference 18

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This paper cites Federated few-shot learning for mobile nlp.

A Robust Federated Learning Framework for Undependable Devices at Scale Federated few-shot learning for mobile nlp

Reference 19

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This paper cites Aut- ofed: Heterogeneity-aware federated multimodal learning for robust autonomous driving.

A Robust Federated Learning Framework for Undependable Devices at Scale Aut- ofed: Heterogeneity-aware federated multimodal learning for robust autonomous driving

Reference 20

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This paper cites In 2024 USENIX Annual Technical Conference (USENIX ATC 24), pages 579–596, 2024.

A Robust Federated Learning Framework for Undependable Devices at Scale In 2024 USENIX Annual Technical Conference (USENIX ATC 24), pages 579–596, 2024

Reference 21

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This paper cites Federated Learning with Non-IID Data.

A Robust Federated Learning Framework for Undependable Devices at Scale Federated Learning with Non-IID Data

Reference 22

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This paper cites Imagenet classification with deep convolutional neural networks.

A Robust Federated Learning Framework for Undependable Devices at Scale Imagenet classification with deep convolutional neural networks

Reference 23

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

A Robust Federated Learning Framework for Undependable Devices at Scale Learning multiple layers of features from tiny images

Reference 24

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This paper cites Multitude of beta distributions with applications.

A Robust Federated Learning Framework for Undependable Devices at Scale Multitude of beta distributions with applications

Reference 25

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This paper cites Bayesian theory, volume 405.

A Robust Federated Learning Framework for Undependable Devices at Scale Bayesian theory, volume 405

Reference 26

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This paper cites Finite-time analysis of the multiarmed bandit problem.

A Robust Federated Learning Framework for Undependable Devices at Scale Finite-time analysis of the multiarmed bandit problem

Reference 27

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This paper cites Towards efficient and stable k-asynchronous federated learning with unbounded stale gradients on non-iid data.

A Robust Federated Learning Framework for Undependable Devices at Scale Towards efficient and stable k-asynchronous federated learning with unbounded stale gradients on non-iid data

Reference 28

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This paper cites Fleet: Online federated learning via staleness awareness and performance prediction.

A Robust Federated Learning Framework for Undependable Devices at Scale Fleet: Online federated learning via staleness awareness and performance prediction

Reference 29

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This paper cites Fedpa: An adaptively partial model aggregation strategy in federated learning.

A Robust Federated Learning Framework for Undependable Devices at Scale Fedpa: An adaptively partial model aggregation strategy in federated learning

Reference 30

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This paper cites Robust asynchronous federated learning with time-weighted and stale model aggregation.

A Robust Federated Learning Framework for Undependable Devices at Scale Robust asynchronous federated learning with time-weighted and stale model aggregation

Reference 31

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This paper cites Hiflash: Communication-efficient hi- erarchical federated learning with adaptive staleness control and heterogeneity-aware client-edge association.

A Robust Federated Learning Framework for Undependable Devices at Scale Hiflash: Communication-efficient hi- erarchical federated learning with adaptive staleness control and heterogeneity-aware client-edge association

Reference 32

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This paper cites https://www.oppo.com/en/smartphones/.

A Robust Federated Learning Framework for Undependable Devices at Scale https://www.oppo.com/en/smartphones/

Reference 33

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This paper cites https://github.com/alibaba/MNN?tab=readme-ov-file.

A Robust Federated Learning Framework for Undependable Devices at Scale https://github.com/alibaba/MNN?tab=readme-ov-file

Reference 34

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This paper cites https://docs.nvidia.com/jetson/.

A Robust Federated Learning Framework for Undependable Devices at Scale https://docs.nvidia.com/jetson/

Reference 35

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This paper cites Docker: lightweight linux containers for consistent development and deployment.

A Robust Federated Learning Framework for Undependable Devices at Scale Docker: lightweight linux containers for consistent development and deployment

Reference 36

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This paper cites Pytorch: An imperative style, high-performance deep learning library.

A Robust Federated Learning Framework for Undependable Devices at Scale Pytorch: An imperative style, high-performance deep learning library

Reference 37

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This paper cites http://dast.nlanr.net/ Projects/Iperf/.

A Robust Federated Learning Framework for Undependable Devices at Scale http://dast.nlanr.net/ Projects/Iperf/

Reference 38

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This paper cites Deep convolutional neural net- works for image classification: A comprehensive review.

A Robust Federated Learning Framework for Undependable Devices at Scale Deep convolutional neural net- works for image classification: A comprehensive review

Reference 39

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This paper cites https://github.com/ymliao98/PS_socket/blob/main/models.py.

A Robust Federated Learning Framework for Undependable Devices at Scale https://github.com/ymliao98/PS_socket/blob/main/models.py

Reference 40

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This paper cites Deep residual learning for image recognition.

A Robust Federated Learning Framework for Undependable Devices at Scale Deep residual learning for image recognition

Reference 41

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This paper cites Biosignal sensors and deep learning-based speech recognition: A review.

A Robust Federated Learning Framework for Undependable Devices at Scale Biosignal sensors and deep learning-based speech recognition: A review

Reference 42

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This paper cites https://pytorch.

A Robust Federated Learning Framework for Undependable Devices at Scale https://pytorch

Reference 43

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This paper cites Speech Commands: A Dataset for Limited-Vocabulary Speech Recognition.

A Robust Federated Learning Framework for Undependable Devices at Scale Speech Commands: A Dataset for Limited-Vocabulary Speech Recognition

Reference 44

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This paper cites On-Device Recommender Systems: A Comprehensive Survey.

A Robust Federated Learning Framework for Undependable Devices at Scale On-Device Recommender Systems: A Comprehensive Survey

Reference 45

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This paper cites Wide & deep learning for recommender systems.

A Robust Federated Learning Framework for Undependable Devices at Scale Wide & deep learning for recommender systems

Reference 46

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This paper cites https://www.kaggle.com/c/avazu-ctr-prediction/ data.

A Robust Federated Learning Framework for Undependable Devices at Scale https://www.kaggle.com/c/avazu-ctr-prediction/ data

Reference 47

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This paper cites Decentralized federated learning: Fundamentals, state of the art, frameworks, trends, and challenges.

A Robust Federated Learning Framework for Undependable Devices at Scale Decentralized federated learning: Fundamentals, state of the art, frameworks, trends, and challenges

Reference 48

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This paper cites Fedlab: A flexible federated learning framework.

A Robust Federated Learning Framework for Undependable Devices at Scale Fedlab: A flexible federated learning framework

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T23:45:18.831642Z

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This paper cites Federated learning for internet of things: Recent advances, taxonomy, and open challenges.

A Robust Federated Learning Framework for Undependable Devices at Scale Federated learning for internet of things: Recent advances, taxonomy, and open challenges

Reference 50

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This paper cites Federated Learning: Opportunities and Challenges.

A Robust Federated Learning Framework for Undependable Devices at Scale Federated Learning: Opportunities and Challenges

Reference 51

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This paper cites Federated optimization in heterogeneous networks.

A Robust Federated Learning Framework for Undependable Devices at Scale Federated optimization in heterogeneous networks

Reference 52

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This paper cites Adaptive federated optimization.

A Robust Federated Learning Framework for Undependable Devices at Scale Adaptive federated optimization

Reference 53

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Observation a96e38d7-5d21-4bd5-9c2e-e2d6b1f77891 · outbound

This paper cites Fedur: Federated learning optimization through adaptive centralized learning optimizers.

A Robust Federated Learning Framework for Undependable Devices at Scale Fedur: Federated learning optimization through adaptive centralized learning optimizers

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T23:45:18.777924Z

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Observation d53eddea-fe2e-427b-8983-5eb5509284f4 · outbound

This paper cites Fedala: Adaptive local aggregation for person- alized federated learning.

A Robust Federated Learning Framework for Undependable Devices at Scale Fedala: Adaptive local aggregation for person- alized federated learning

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T23:45:18.763943Z

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Observation 22b4416a-0b0b-403f-a7fa-c5a1fdedc209 · outbound

This paper cites Towards personalized federated learning via heterogeneous model reassembly.

A Robust Federated Learning Framework for Undependable Devices at Scale Towards personalized federated learning via heterogeneous model reassembly

Reference 56

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Observation bc4ea047-0f9c-4e58-a8bc-fa64a8c921f3 · outbound

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A Robust Federated Learning Framework for Undependable Devices at Scale Flow: per-instance personalized federated learning

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T23:45:18.740685Z

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

Observation 4e2bb511-3c63-4240-8b9e-aeddc5c67e7c · outbound

This paper cites Fedas: Bridging inconsis- tency in personalized federated learning.

A Robust Federated Learning Framework for Undependable Devices at Scale Fedas: Bridging inconsis- tency in personalized federated learning

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T23:45:18.727175Z

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

Observation 0586f9c2-f22a-4b27-8dd6-c1a071787a65 · outbound

This paper cites Communication Efficiency in Federated Learning: Achievements and Challenges.

A Robust Federated Learning Framework for Undependable Devices at Scale Communication Efficiency in Federated Learning: Achievements and Challenges

Reference 59

Resolution: unresolved; no resolver link, observed 2026-08-10T23:45:18.348131Z

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Observation 1bd651a4-6dc5-4965-8d58-322b33f72b5b · outbound

This paper cites Uveqfed: Universal vector quantization for federated learning.

A Robust Federated Learning Framework for Undependable Devices at Scale Uveqfed: Universal vector quantization for federated learning

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T23:45:18.712736Z

Source-reported events for the cited work

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Observation 4e21fe0f-911e-4c03-84b0-a14281684dbe · outbound

This paper cites Adaptive quantization of model updates for communication- efficient federated learning.

A Robust Federated Learning Framework for Undependable Devices at Scale Adaptive quantization of model updates for communication- efficient federated learning

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T23:45:18.698749Z

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Observation 964420aa-40d6-4230-9d7d-a106c45f775a · outbound

This paper cites Towards mitigating device heterogeneity in federated learning via adaptive model quantization.

A Robust Federated Learning Framework for Undependable Devices at Scale Towards mitigating device heterogeneity in federated learning via adaptive model quantization

Reference 62

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T23:45:18.684661Z

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 d572a2d2-a613-40a2-bd49-af3b9d8ff8f5 · outbound

This paper cites Neural network quantization in federated learn- ing at the edge.

A Robust Federated Learning Framework for Undependable Devices at Scale Neural network quantization in federated learn- ing at the edge

Reference 63

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T23:45:18.671735Z

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 7d3f97e0-38c5-42b7-9fd6-12eb932acddc · outbound

This paper cites Federated Learning With Quantized Global Model Updates.

A Robust Federated Learning Framework for Undependable Devices at Scale Federated Learning With Quantized Global Model Updates

Reference 64

Resolution: unresolved; no resolver link, observed 2026-08-10T23:45:18.372942Z

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Observation 03e1ae57-4957-422a-a0a1-ceff8f318976 · outbound

This paper cites Model compression for communication efficient federated learning.

A Robust Federated Learning Framework for Undependable Devices at Scale Model compression for communication efficient federated learning

Reference 65

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T23:45:18.657710Z

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

Observation 51785202-a669-4204-b975-24203f34212f · outbound

This paper cites FEDZIP: A Compression Framework for Communication-Efficient Federated Learning.

A Robust Federated Learning Framework for Undependable Devices at Scale FEDZIP: A Compression Framework for Communication-Efficient Federated Learning

Reference 66

Resolution: unresolved; no resolver link, observed 2026-08-10T23:45:18.382400Z

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Observation 7bd0ddbe-9573-40d8-bc07-29e8097d20ce · outbound

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A Robust Federated Learning Framework for Undependable Devices at Scale Unresolved cited work

Reference 67

Resolution: unresolved; raw_fallback, observed 2026-08-10T23:45:18.644756Z

Source-reported events for the cited work

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Observation 6c63ba58-4a1d-442f-9e83-2da6d765a755 · outbound

This paper cites FedMD: Heterogenous Federated Learning via Model Distillation.

A Robust Federated Learning Framework for Undependable Devices at Scale FedMD: Heterogenous Federated Learning via Model Distillation

Reference 68

Resolution: unresolved; no resolver link, observed 2026-08-10T23:45:18.391443Z

Source-reported events for the cited work

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Observation 14e92361-d43f-46c8-86af-35d11d23e076 · outbound

This paper cites Data-free knowledge distillation for heterogeneous federated learning.

A Robust Federated Learning Framework for Undependable Devices at Scale Data-free knowledge distillation for heterogeneous federated learning

Reference 69

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T23:45:18.629266Z

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 99050970-9bd1-4457-b3ba-fd749ceb387a · outbound

This paper cites Communication-efficient federated learning via knowledge distil- lation.

A Robust Federated Learning Framework for Undependable Devices at Scale Communication-efficient federated learning via knowledge distil- lation

Reference 70

Resolution: unresolved; no resolver link, observed 2026-08-10T23:45:18.401061Z

Source-reported events for the cited work

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Observation 59ed01ad-2324-409e-94a6-a90c825931d1 · outbound

This paper cites Fedfed: Feature distillation against data heterogeneity in federated learning.

A Robust Federated Learning Framework for Undependable Devices at Scale Fedfed: Feature distillation against data heterogeneity in federated learning

Reference 71

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T23:45:18.608531Z

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 424f6b5a-f821-4b79-b62c-1dbbc30aa320 · outbound

This paper cites Dfrd: Data-free robustness distillation for heterogeneous federated learning.

A Robust Federated Learning Framework for Undependable Devices at Scale Dfrd: Data-free robustness distillation for heterogeneous federated learning

Reference 72

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T23:45:18.594050Z

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.

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