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Adaptive Latent-Space Constraints in Personalized Federated Learning

arXiv 2505.07525 ↗First posted 12 May 2025cs.LGcited by 0

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Abstract (arXiv)

Federated learning (FL) is an effective and widely used approach to training deep learning models on decentralized datasets held by distinct clients. FL also strengthens both security and privacy protections for training data. Common challenges associated with statistical heterogeneity between distributed datasets have spurred significant interest in personalized FL (pFL) methods, where models combine aspects of global learning with local modeling specific to each client's unique characteristics. This work investigates the efficacy of theoretically supported, adaptive MMD measures in pFL, primarily focusing on the Ditto framework, a state-of-the-art technique for distributed data heterogeneity. The use of such measures significantly improves model performance across a variety of tasks, especially those with pronounced feature heterogeneity. Additional experiments demonstrate that such measures are directly applicable to other pFL techniques and yield similar improvements across a number of datasets. Finally, the results motivate the use of constraints tailored to the various kinds of heterogeneity expected in FL systems.

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References · 36

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24 more
  1. 2021FedBN: Federated Learning on Non-IID Features via Local Batch Normalizationcited by 571
  2. 2019Federated Learning with Personalization Layerscited by 541
  3. 2021Exploiting Shared Representations for Personalized Federated Learningcited by 541
  4. 2020Adaptive Personalized Federated Learningcited by 358
  5. 2021FedProto: Federated Prototype Learning across Heterogeneous Clientscited by 352
  6. 2020HeteroFL: Computation and Communication Efficient Federated Learning for Heterogeneous Clientscited by 337
  7. 2020Three Approaches for Personalization with Applications to Federated Learningcited by 325
  8. 2020Attack of the Tails: Yes, You Really Can Backdoor Federated Learningcited by 309
  9. 2019SMART: Robust and Efficient Fine-Tuning for Pre-trained Natural Language Models through Principled Regularized Optimizationcited by 254
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  13. 2022FLamby: Datasets and Benchmarks for Cross-Silo Federated Learning in Realistic Healthcare Settingscited by 125
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  16. 2023RxRx1: A Dataset for Evaluating Experimental Batch Correction Methodscited by 46
  17. 2022pFL-Bench: A Comprehensive Benchmark for Personalized Federated Learningcited by 44
  18. 2022On Privacy and Personalization in Cross-Silo Federated Learningcited by 36
  19. 2022FedPop: A Bayesian Approach for Personalised Federated Learningcited by 35
  20. 2022FRAug: Tackling Federated Learning with Non-IID Features via Representation Augmentationcited by 24
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  22. 2022Training Mixed-Domain Translation Models via Federated Learningcited by 12
  23. 2021FedMe: Federated Learning via Model Exchangecited by 10
  24. 2023A Comprehensive View of Personalized Federated Learning on Heterogeneous Clinical Datasetscited by 1

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