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Personalized Federated Learning with Contextual Modulation and Meta-Learning

arXiv 2312.15191 ↗First posted 23 Dec 2023cs.LGcited by 0

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Abstract (arXiv)

Federated learning has emerged as a promising approach for training machine learning models on decentralized data sources while preserving data privacy. However, challenges such as communication bottlenecks, heterogeneity of client devices, and non-i.i.d. data distribution pose significant obstacles to achieving optimal model performance. We propose a novel framework that combines federated learning with meta-learning techniques to enhance both efficiency and generalization capabilities. Our approach introduces a federated modulator that learns contextual information from data batches and uses this knowledge to generate modulation parameters. These parameters dynamically adjust the activations of a base model, which operates using a MAML-based approach for model personalization. Experimental results across diverse datasets highlight the improvements in convergence speed and model performance compared to existing federated learning approaches. These findings highlight the potential of incorporating contextual information and meta-learning techniques into federated learning, paving the way for advancements in distributed machine learning paradigms.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 37 of its 47 references that are in the map. First posted on arXiv 23 Dec 2023.

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37 of 47 references.

References · 37

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25 more
  1. 2020Federated Learning with Matched Averagingcited by 564
  2. 2021Federated Learning Based on Dynamic Regularizationcited by 562
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