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Federated learning (FL) has been widely adopted for collaborative training on decentralized data.
FedAPEN: Personalized Cross-silo Federated Learning with Adaptability to Statistical Heterogeneity. In Proc. KDD . ACM, Long Beach, CA, USA, 1954–1964
Zhen Qin et al · 1964
Earlier work this paper cites.
Visualizing Data using t-SNE
Laurens van der Maaten and Geoffrey Hinton. 2008 · 2008
Earlier work this paper cites.
Learning multiple layers of features from tiny images
Alex Krizhevsky et al · 2009
Earlier work this paper cites.
An overview of gradient descent optimization algorithms
Sebastian Ruder. 2016 · 2016
Earlier work this paper cites.
Communication-Efficient Learning of Deep Networks from Decentralized Data. In Proc. AISTATS , Vol. 54. PMLR, Fort Lauderdale, FL, USA, 1273–1282
Brendan McMahan et al · 2017
Earlier work this paper cites.
Communication-Efficient On-Device Machine Learning: Federated Distillation and Augmentation under Non-IID Private Data. In Proc. NeurIPS Workshop on Machine Learning on the Phone and other Consumer Devices . , virtual
Eunjeong Jeong et al · 2018
Earlier work this paper cites.
Generalized Cross Entropy Loss for Training Deep Neural Networks with Noisy Labels. In Proc. NeurIPS . Curran Associates Inc., Montréal, Canada, 8792–8802
Zhilu Zhang and Mert R. Sabuncu. 2018 · 2018
Earlier work this paper cites.
Wireless Federated Distillation for Distributed Edge Learning with Heterogeneous Data. In Proc. PIMRC . IEEE, Istanbul, Turkey, 1–6
Jin-Hyun Ahn et al · 2019
Earlier work this paper cites.
FedMD: Heterogenous Federated Learning via Model Distillation. In Proc. NeurIPS Workshop . , virtual
Daliang Li and Junpu Wang. 2019 · 2019
Earlier work this paper cites.
Differentiable Learning-to-Normalize via Switchable Normalization. In Proc. ICLR . OpenReview.net, New Orleans, LA, USA, 1
Ping Luo et al · 2019
Earlier work this paper cites.
Cooperative Learning VIA Federated Distillation OVER Fading Channels. In Proc. ICASSP . IEEE, Barcelona, Spain, 8856–8860
Jin-Hyun Ahn et al · 2020
Earlier work this paper cites.
Group Knowledge Transfer: Federated Learning of Large CNNs at the Edge. In Proc. NeurIPS . , virtual
Chaoyang He et al · 2020
Earlier work this paper cites.
Multi-site fMRI analysis using privacy-preserving federated learning and domain adaptation: ABIDE results
Xiaoxiao Li et al · 2020
Earlier work this paper cites.
Think locally, act globally: Federated learning with local and global representations
Paul Pu Liang et al · 2020
Earlier work this paper cites.
Ensemble Distillation for Robust Model Fusion in Federated Learning. In Proc. NeurIPS . , virtual
Tao Lin et al · 2020
Earlier work this paper cites.
Tao Shen et al · 2020
Earlier work this paper cites.
Federated learning using a mixture of experts
Edvin Listo Zec et al · 2020
Earlier work this paper cites.
Cronus: Robust and Heterogeneous Collaborative Learning with Black-Box Knowledge Transfer. In Proc. NeurIPS Workshop . , virtual
Hongyan Chang et al · 2021
Earlier work this paper cites.
FedMatch: Federated Learning Over Heterogeneous Question Answering Data. In Proc. CIKM . ACM, virtual, 181–190
Jiangui Chen et al · 2021
Earlier work this paper cites.
FedGEMS: Federated Learning of Larger Server Models via Selective Knowledge Fusion
Sijie Cheng et al · 2021
Cited alongside, same era.
HeteroFL: Computation and Communication Efficient Federated Learning for Heterogeneous Clients. In Proc. ICLR . OpenReview.net, Virtual Event, Austria, 1
Enmao Diao. 2021 · 2021
Cited alongside, same era.
PFL-MoE: Personalized Federated Learning Based on Mixture of Experts. In Proc. APWeb-WAIM , Vol. 12858. Springer, Guangzhou, China, 480–486
Binbin Guo et al · 2021
Cited alongside, same era.
FjORD: Fair and Accurate Federated Learning under heterogeneous targets with Ordered Dropout. In Proc. NIPS . OpenReview.net, Virtual, 12876–12889
S. Horváth. 2021 · 2021
Cited alongside, same era.
Advances and Open Problems in Federated Learning
Peter Kairouz et al · 2021
Cited alongside, same era.
Federated Learning with Partial Model Personalization. In Proc. ICML , Vol. 162. PMLR, virtual, 17716–17758
Krishna Pillutla et al · 2022
Later among the works it cites.
FedProto: Federated Prototype Learning across Heterogeneous Clients. In Proc. AAAI . AAAI Press, virtual, 8432–8440
Yue Tan et al · 2022
Later among the works it cites.
Resource-aware Federated Learning using Knowledge Extraction and Multi-model Fusion
Sixing Yu et al · 2022
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FedZKT: Zero-Shot Knowledge Transfer towards Resource-Constrained Federated Learning with Heterogeneous On-Device Models. In Proc. ICDCS . IEEE, virtual, 928–938
Lan Zhang et al · 2022
Later among the works it cites.
Resilient and Communication Efficient Learning for Heterogeneous Federated Systems. In Proc. ICML , Vol. 162. PMLR, virtual, 27504–27526
Zhuangdi Zhu et al · 2022
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Practical One-Shot Federated Learning for Cross-Silo Setting. In Proc. IJCAI . ijcai.org, virtual, 1484–1490
Qinbin Li et al · 2021
Cited alongside, same era.
Matthias Reisser et al · 2021
Cited alongside, same era.
FEDAUX: Leveraging Unlabeled Auxiliary Data in Federated Learning
Felix Sattler et al · 2021
Cited alongside, same era.
Personalized Federated Learning using Hypernetworks. In Proc. ICML , Vol. 139. PMLR, virtual, 9489–9502
Aviv Shamsian et al · 2021
Cited alongside, same era.
Parameterized Knowledge Transfer for Personalized Federated Learning. In Proc. NeurIPS . OpenReview.net, virtual, 10092–10104
Jie Zhang et al · 2021
Cited alongside, same era.
Data-Free Knowledge Distillation for Heterogeneous Federated Learning. In Proc. ICML , Vol. 139. PMLR, virtual, 12878–12889
Zhuangdi Zhu et al · 2021
Cited alongside, same era.
FedRolex: Model-Heterogeneous Federated Learning with Rolling Sub-Model Extraction. In Proc. NeurIPS . , virtual
Samiul Alam et al · 2022
Cited alongside, same era.
Later among the works it cites.
Revisiting Sparsity Hunting in Federated Learning: Why does Sparsity Consensus Matter?
Sara Babakniya et al · 2023
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Efficient Personalized Federated Learning via Sparse Model-Adaptation. In Proc. ICML , Vol. 202. PMLR, Honolulu, Hawaii, USA, 5234–5256
Daoyuan Chen et al · 2023
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FedJETs: Efficient Just-In-Time Personalization with Federated Mixture of Experts
Chen Dun et al · 2023
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Distillation-Based Semi-Supervised Federated Learning for Communication-Efficient Collaborative Training With Non-IID Private Data
Sohei Itahara et al · 2023
Later among the works it cites.
Enhancing Heterogeneous Federated Learning with Knowledge Extraction and Multi-Model Fusion. In Proc. SC Workshop . ACM, Denver, CO, USA, 36–43
Duy Phuong Nguyen et al · 2023
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Towards Understanding Ensemble Distillation in Federated Learning. In Proc. ICML , Vol. 202. PMLR, Honolulu, Hawaii, USA, 27132–27187
Sejun Park et al · 2023
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Towards Personalized Federated Learning via Heterogeneous Model Reassembly. In Proc. NeurIPS . OpenReview.net, New Orleans, Louisiana, USA, 13
Jiaqi Wang et al · 2023
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FedGH: Heterogeneous Federated Learning with Generalized Global Header. In Proceedings of the 31st ACM International Conference on Multimedia (ACM MM’23) . ACM, Canada, 11
Liping Yi, Gang Wang, Xiaoguang Liu, Zhuan Shi, and Han Yu. 2023 · 2023
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Towards Data-Independent Knowledge Transfer in Model-Heterogeneous Federated Learning
Jie Zhang et al · 2023
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Communication-efficient federated learning via knowledge distillation
Chuhan Wu et al · 2032
Closest in time.
CFD: Communication-Efficient Federated Distillation via Soft-Label Quantization and Delta Coding
Felix Sattler et al · 2038
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Fed2: Feature-Aligned Federated Learning. In Proc. KDD . ACM, virtual, 2066–2074
Fuxun Yu et al · 2074
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Exploiting Shared Representations for Personalized Federated Learning. In Proc. ICML , Vol. 139. PMLR, virtual, 2089–2099
Liam Collins et al · 2099
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