Client Selection in Federated Learning: Convergence Analysis and Power-of-Choice Selection Strategies (2010.01243v1)

Abstract: Federated learning is a distributed optimization paradigm that enables a large number of resource-limited client nodes to cooperatively train a model without data sharing. Several works have analyzed the convergence of federated learning by accounting of data heterogeneity, communication and computation limitations, and partial client participation. However, they assume unbiased client participation, where clients are selected at random or in proportion of their data sizes. In this paper, we present the first convergence analysis of federated optimization for biased client selection strategies, and quantify how the selection bias affects convergence speed. We reveal that biasing client selection towards clients with higher local loss achieves faster error convergence. Using this insight, we propose Power-of-Choice, a communication- and computation-efficient client selection framework that can flexibly span the trade-off between convergence speed and solution bias. Our experiments demonstrate that Power-of-Choice strategies converge up to 3 $\times$ faster and give $10$% higher test accuracy than the baseline random selection.

Client Selection in Federated Learning: An Analytical Perspective

The paper at hand presents an in-depth exploration of client selection strategies within Federated Learning (FL), a model of distributed optimization which circumvents the need for data sharing. By facilitating cooperative model training across numerous client nodes, FL inherently encounters hurdles associated with data heterogeneity, limited communication bandwidth, and constrained computational resources. Central to this paper is the unique examination of biased client selection, which has not been previously undertaken in such comprehensive detail.

Key Contributions

The paper innovatively deviates from conventional assumptions of unbiased client participation, where clients are selected at random or in proportion to their dataset size. Instead, it pioneers the first known convergence analysis of federated optimization that takes into account biased client selection strategies. Evidence from the analysis reveals that introducing bias towards clients that present higher local loss can significantly accelerate the rate of error convergence.

Building on these findings, the authors introduce the $Power-of-choice$ strategy. This is a sophisticated, yet computationally efficient client selection framework that permits a controlled balance between convergence speed and solution bias. It is distinctive in offering the flexibility to fine-tune this trade-off, making it highly adaptable to varying FL environments. Empirical evaluations demonstrate that the $Power-of-choice$ method achieves up to a $3 \times$ faster convergence rate with a $10\%$ improvement in test accuracy over the standard random selection baseline.

Analytical and Experimental Insights

The convergence analysis, performed under both decaying and fixed learning rates, is methodologically rigorous. It uncovers that a larger selection skew leads to faster convergence thanks to preferential treatment of clients with higher local losses — this holds without a significant increase in the non-vanishing bias term. Importantly, the paper's investigation into $Power-of-choice$ illustrates that it can achieve enhanced convergence without necessarily increasing the number of participating clients, thus maintaining efficiency in communication and computation.

The paper also explores variations of the $Power-of-choice$ strategy, including computation-efficient and communication-efficient adaptations. These are vital for deployment in resource-constrained settings and maintain a high level of accuracy with minimal overhead.

Practical and Theoretical Implications

The findings have considerable implications for the design of FL systems. By quantifying the impacts of client selection bias, the research provides a valuable framework for optimizing FL processes in environments characterized by data and computational heterogeneity. Practically, these insights could be leveraged to enhance the performance of FL in real-world applications such as edge computing and mobile networks, where client resources and availability are heterogeneous and dynamic.

Theoretically, the paper enriches the understanding of convergence dynamics in FL and opens avenues for further research into adaptive and intelligent client selection mechanisms. By paving the way for more nuanced models of client participation, the research contributes to the robustness and efficacy of federated learning.

Future Directions

Possible extensions to this work include further refinement of client selection strategies considering fairness and robustness, addressing the challenges posed by non-iid data distributions, and exploring adaptive mechanisms that account for temporal changes in client availability and capacity. The interplay between convergence speed, resource allocation, and solution bias remains a fertile ground for future investigation.

In concluding, this paper marks a significant step toward optimizing client selection strategies in federated learning, with clear guidelines on leveraging biased client selection to meet specific system requirements.