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Personalized Federated Learning with Contextualized Generalization

arXiv 2106.13044 ↗First posted 24 Jun 2021cs.LG cs.DCcited by 23

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Abstract (arXiv)

The prevalent personalized federated learning (PFL) usually pursues a trade-off between personalization and generalization by maintaining a shared global model to guide the training process of local models. However, the sole global model may easily transfer deviated context knowledge to some local models when multiple latent contexts exist across the local datasets. In this paper, we propose a novel concept called contextualized generalization (CG) to provide each client with fine-grained context knowledge that can better fit the local data distributions and facilitate faster model convergence, based on which we properly design a framework of PFL, dubbed CGPFL. We conduct detailed theoretical analysis, in which the convergence guarantee is presented and O(√K) speedup over most existing methods is granted. To quantitatively study the generalization-personalization trade-off, we introduce the 'generalization error' measure and prove that the proposed CGPFL can achieve a better trade-off than existing solutions. Moreover, our theoretical analysis further inspires a heuristic algorithm to find a near-optimal trade-off in CGPFL. Experimental results on multiple real-world datasets show that our approach surpasses the state-of-the-art methods on test accuracy by a significant margin.

Key facts. Cited by 23 papers in the map (data through 7 Oct 2026). Builds on 18 of its 32 references that are in the map. First posted on arXiv 24 Jun 2021.

Builds on

18 of 32 references.

References · 18

  1. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,592
  2. 2017Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithmscited by 4,283
  3. 2019Advances and Open Problems in Federated Learningcited by 3,021
  4. 2020Adaptive Federated Optimizationcited by 1,036
  5. 2017Federated Multi-Task Learningcited by 669
  6. 2020Ditto: Fair and Robust Federated Learning Through Personalizationcited by 665
  7. 2020Personalized Federated Learning with Moreau Envelopescited by 612
  8. 2019Federated Learning with Personalization Layerscited by 533
  9. 2020An Efficient Framework for Clustered Federated Learningcited by 505
  10. 2019Clustered Federated Learning: Model-Agnostic Distributed Multi-Task Optimization under Privacy Constraintscited by 499
  11. 2019FedMD: Heterogenous Federated Learning via Model Distillationcited by 454
  12. 2020Adaptive Personalized Federated Learningcited by 353
6 more
  1. 2020Three Approaches for Personalization with Applications to Federated Learningcited by 321
  2. 2019The Non-IID Data Quagmire of Decentralized Machine Learningcited by 303
  3. 2020Federated learning with hierarchical clustering of local updates to improve training on non-IID datacited by 222
  4. 2020Federated Learning of a Mixture of Global and Local Modelscited by 208
  5. 2020Lower Bounds and Optimal Algorithms for Personalized Federated Learningcited by 122
  6. 2018A Tight Convergence Analysis for Stochastic Gradient Descent with Delayed Updatescited by 47

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Cited by year

Citing papers per year: 2021: 1; 2022: 3; 2023: 7; 2024: 8; 2025: 4.

23 papers.

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  1. 2022PromptFL: Let Federated Participants Cooperatively Learn Prompts Instead of Models -- Federated Learning in Age of Foundation Modelcited by 133
  2. 2024FedAC: An Adaptive Clustered Federated Learning Framework for Heterogeneous Datacited by 25
  3. 2023Bold but Cautious: Unlocking the Potential of Personalized Federated Learning through Cautiously Aggressive Collaborationcited by 23
  4. 2025LCFed: An Efficient Clustered Federated Learning Framework for Heterogeneous Datacited by 15
  5. 2022Fed-FSNet: Mitigating Non-I.I.D. Federated Learning via Fuzzy Synthesizing Networkcited by 14
  6. 2023Multimodal Federated Learning in Healthcare: a Reviewcited by 13
  7. 2024Neuron: Learning Context-Aware Evolving Representations for Zero-Shot Skeleton Action Recognitioncited by 13
  8. 2024Fine-Grained Zero-Shot Learning: Advances, Challenges, and Prospectscited by 12
  9. 2023Towards Fairer and More Efficient Federated Learning via Multidimensional Personalized Edge Modelscited by 10
  10. 2021From Deterioration to Acceleration: A Calibration Approach to Rehabilitating Step Asynchronism in Federated Optimizationcited by 8
  11. 2023GBE-MLZSL: A Group Bi-Enhancement Framework for Multi-Label Zero-Shot Learningcited by 8
  12. 2024TsCA: On the Semantic Consistency Alignment via Conditional Transport for Compositional Zero-Shot Learningcited by 6
11 more
  1. 2022Exploiting Personalized Invariance for Better Out-of-distribution Generalization in Federated Learningcited by 4
  2. 2024Dual Expert Distillation Network for Generalized Zero-Shot Learningcited by 4
  3. 2024The Diversity Bonus: Learning from Dissimilar Distributed Clients in Personalized Federated Learningcited by 3
  4. 2024Epsilon: Exploring Comprehensive Visual-Semantic Projection for Multi-Label Zero-Shot Learningcited by 3
  5. 2023Personalized Federated Learning via Gradient Modulation for Heterogeneous Text Summarizationcited by 1
  6. 2024Federated Learning Can Find Friends That Are Advantageouscited by 1
  7. 2025FedBKD: Distilled Federated Learning to Embrace Gerneralization and Personalization on Non-IID Datacited by 1
  8. 2023IP-FL: Incentivized and Personalized Federated Learningcited by 0
  9. 2023Entropy Aware Training for Fast and Accurate Distributed GNNcited by 0
  10. 2025FedAPA: Server-side Gradient-Based Adaptive Personalized Aggregation for Federated Learning on Heterogeneous Datacited by 0
  11. 2025FLStore: Efficient Federated Learning Storage for non-training workloadscited by 0