Local Learning Matters: Rethinking Data Heterogeneity in Federated Learning (2111.14213v3)

Abstract: Federated learning (FL) is a promising strategy for performing privacy-preserving, distributed learning with a network of clients (i.e., edge devices). However, the data distribution among clients is often non-IID in nature, making efficient optimization difficult. To alleviate this issue, many FL algorithms focus on mitigating the effects of data heterogeneity across clients by introducing a variety of proximal terms, some incurring considerable compute and/or memory overheads, to restrain local updates with respect to the global model. Instead, we consider rethinking solutions to data heterogeneity in FL with a focus on local learning generality rather than proximal restriction. To this end, we first present a systematic study informed by second-order indicators to better understand algorithm effectiveness in FL. Interestingly, we find that standard regularization methods are surprisingly strong performers in mitigating data heterogeneity effects. Based on our findings, we further propose a simple and effective method, FedAlign, to overcome data heterogeneity and the pitfalls of previous methods. FedAlign achieves competitive accuracy with state-of-the-art FL methods across a variety of settings while minimizing computation and memory overhead. Code is available at https://github.com/mmendiet/FedAlign

• The paper introduces a novel approach that emphasizes local learning generality over traditional proximal methods to effectively mitigate data heterogeneity.
• It proposes FedAlign, a technique that aligns network layer Lipschitz constants via distillation, reducing computational overhead and client drift.
• Empirical results on CIFAR-100, CIFAR-10, and ImageNet-200 demonstrate that the method achieves state-of-the-art accuracy with improved resource efficiency.

An Analytical Overview of "Local Learning Matters: Rethinking Data Heterogeneity in Federated Learning"

Federated learning (FL) is a distributed machine learning paradigm designed to train models across decentralized data while ensuring data privacy. A significant challenge arises from data heterogeneity, wherein the data distribution among clients is non-IID, hindering effective optimization. The authors of "Local Learning Matters: Rethinking Data Heterogeneity in Federated Learning" propose an innovative approach to address data heterogeneity by prioritizing local learning generality over traditional proximal gradient methods, effectively balancing performance and computational efficiency in federated systems.

Rethinking Data Heterogeneity in FL

Traditional efforts in FL for mitigating data heterogeneity have involved integrating proximal terms to restrict local model updates with respect to the global model. While these methods aim to alleviate client drift, they often come at the cost of increased computational and memory overhead, restricting local convergence and efficiency. This paper introduces a new perspective by focusing on enhancing local learning generality to bridge the gap between local and global models without incurring excessive overhead.

Empirical and Theoretical Insights

The paper systematically evaluates the effectiveness of various regularization methods in improving FL performance, employing both empirical studies and theoretical analyses based on second-order information. Remarkably, it finds that generalized regularization techniques like Mixup, Stochastic Depth, and GradAug deliver superior performance over existing FL optimizers like MOON and FedProx, particularly under non-IID data conditions. Notably, these regularization methods align better with the concept of out-of-distribution generalization, suggesting that client drift in FL can be significantly controlled by improving generalization rather than merely constraining updates.

The FedAlign Methodology

From these insights, the authors propose FedAlign, a regularization approach that aligns the Lipschitz constants of the final network layers using a distillation-based technique. By focusing on the most overfit-prone segments of a model—the final layers—FedAlign minimizes additional resource needs and optimizes performance. Unlike other FL strategies, FedAlign promotes local learning generality through spectral norm mapping of network block transformations, effectively minimizing both computational and memory requirements.

Experimental Validation and Analysis

Experiments conducted across datasets such as CIFAR-100, CIFAR-10, and a subset of ImageNet (ImageNet-200) robustly validate FedAlign’s efficacy. The method achieves state-of-the-art accuracy comparable to or surpassing existing methods, with reduced computation and communication costs, demonstrating remarkable efficiency and adaptability across FL settings.

Implications and Future Directions

This paper challenges existing FL paradigms by showcasing that a focus on local learning generality can effectively mitigate data heterogeneity without substantial computational burden. The approach complements efforts to improve FL systems, encouraging a reconsideration of regularization in both client-side training and global aggregation. Future advancements could explore extending FedAlign to non-vision FL applications or incorporating other dimensions of learning theory in its framework.

The work provides a pivotal step forward in federated learning research, illustrating that fostering broader generalization is crucial for addressing client drift, and it proposes an elegant solution with FedAlign, balancing state-of-the-art performance with resource efficiency.