Adaptive Personalized Federated Learning (2003.13461v3)

Abstract: Investigation of the degree of personalization in federated learning algorithms has shown that only maximizing the performance of the global model will confine the capacity of the local models to personalize. In this paper, we advocate an adaptive personalized federated learning (APFL) algorithm, where each client will train their local models while contributing to the global model. We derive the generalization bound of mixture of local and global models, and find the optimal mixing parameter. We also propose a communication-efficient optimization method to collaboratively learn the personalized models and analyze its convergence in both smooth strongly convex and nonconvex settings. The extensive experiments demonstrate the effectiveness of our personalization schema, as well as the correctness of established generalization theories.

Adaptive Personalized Federated Learning: A Comprehensive Overview

The paper "Adaptive Personalized Federated Learning" presents a novel algorithm that addresses personalization in federated learning (FL) scenarios. This research underlines the limitations of solely optimizing global models and proposes a personalized federated learning (APFL) algorithm that balances local and global model contributions for enhanced personalization.

Background and Motivation

Federated learning seeks to train a high-quality shared model across decentralized devices, reducing privacy risks and communication costs. However, this centralized approach is often hindered by the inherent diversity and non-IID nature of local data shards, which limits the performance of purely global models. In order to address these shortcomings, this paper introduces a methodology that can accommodate individual client needs by adjusting the contribution of global and local models.

Proposed Approach

The cornerstone of this work is the Adaptive Personalized Federated Learning (APFL) algorithm. The authors introduce a personalized model that linearly combines optimal local and global models, governed by a mixing parameter $\alpha_i$. This balances the generalization performance benefits from shared data with the specific needs of local data.

The paper also proposes a communication-efficient optimization method that adapts the local models by leveraging the relationship between local and global data throughout the learning process. This optimization strategy is meticulously analyzed in both smooth strongly convex and non-convex settings.

Theoretical Analysis

The theoretical contributions include characterizations of the generalization ability of personalized models. The researchers derive a generalization bound that explicitly considers the divergence between local and global distributions and the volume of local and global data. These bounds reveal the conditions under which personalization adds value over a purely global model.

Furthermore, the research establishes convergence results for the APFL algorithm. In the case of strongly convex functions, the global model convergence matches leading federated learning algorithms, such as local SGD. For non-convex functions, the convergence is assessed through gradient norms, accounting for gradient discrepancy and diversity among clients.

Experimental Results

Comprehensive experiments across various datasets, including MNIST and CIFAR10, demonstrate APFL’s superiority over existing global and locally fine-tuned models. Specifically, APFL excels in scenarios with high data diversity among clients. Notably, results indicate that adaptively tuning the mixing parameter $\alpha_i$ based on ongoing learning dynamics significantly enhances both optimization and generalization performance, particularly under non-IID conditions.

Practical and Theoretical Implications

Practically, APFL offers a robust personalized solution for federated environments, making it suitable for privacy-critical applications like healthcare. Theoretically, the paper enriches the federated learning landscape by proposing bounds and optimization techniques that accommodate personalization through a mixed-model framework.

Future Directions

Potential future work includes applying APFL in agnostic federated settings where models must be robust across unknown distributions and exploring richer personalization frameworks that further minimize negative transfer between local and global learning objectives.

Conclusion

This paper significantly contributes to federated learning by enabling personalized adaptations that cater to client-specific data distributions. Through both theoretical insights and empirical validation, APFL emerges as a compelling approach that mitigates the limitations of traditional federated learning models.