Personalized Federated Learning with Contextual Modulation and Meta-Learning
This work addresses the problem of optimizing federated learning for distributed machine learning applications, offering an incremental improvement by integrating contextual modulation and meta-learning techniques.
The paper tackles the problem of improving federated learning by addressing challenges like communication bottlenecks and non-i.i.d. data, proposing a framework that combines federated learning with meta-learning to enhance efficiency and generalization, resulting in improved convergence speed and model performance across diverse datasets.
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.