Personalized Federated Learning with Contextual Modulation and Meta-Learning

TL;DR

This paper introduces a federated learning framework that leverages meta-learning and contextual modulation to improve model personalization, efficiency, and performance across decentralized, heterogeneous data sources.

Contribution

It presents a novel federated meta-learning approach with a contextual modulator that dynamically adjusts model activations for personalized federated learning.

Findings

Faster convergence compared to existing methods

Enhanced model performance on diverse datasets

Effective handling of data heterogeneity

Abstract

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.