pFL-Bench: A Comprehensive Benchmark for Personalized Federated Learning (2206.03655v4)

Abstract: Personalized Federated Learning (pFL), which utilizes and deploys distinct local models, has gained increasing attention in recent years due to its success in handling the statistical heterogeneity of FL clients. However, standardized evaluation and systematical analysis of diverse pFL methods remain a challenge. Firstly, the highly varied datasets, FL simulation settings and pFL implementations prevent easy and fair comparisons of pFL methods. Secondly, the current pFL literature diverges in the adopted evaluation and ablation protocols. Finally, the effectiveness and robustness of pFL methods are under-explored in various practical scenarios, such as the generalization to new clients and the participation of resource-limited clients. To tackle these challenges, we propose the first comprehensive pFL benchmark, pFL-Bench, for facilitating rapid, reproducible, standardized and thorough pFL evaluation. The proposed benchmark contains more than 10 dataset variants in various application domains with a unified data partition and realistic heterogeneous settings; a modularized and easy-to-extend pFL codebase with more than 20 competitive pFL method implementations; and systematic evaluations under containerized environments in terms of generalization, fairness, system overhead, and convergence. We highlight the benefits and potential of state-of-the-art pFL methods and hope the pFL-Bench enables further pFL research and broad applications that would otherwise be difficult owing to the absence of a dedicated benchmark. The code is released at https://github.com/alibaba/FederatedScope/tree/master/benchmark/pFL-Bench.

An Academic Review of "pFL-Bench: A Comprehensive Benchmark for Personalized Federated Learning"

The paper "pFL-Bench: A Comprehensive Benchmark for Personalized Federated Learning" addresses significant challenges in the standardized evaluation of Personalized Federated Learning (pFL) methods. Personalized Federated Learning has emerged as a crucial paradigm for handling statistical heterogeneity among clients in a federated learning (FL) system, yet systematic comparisons remain difficult due to varied datasets, methodologies, and evaluation protocols.

Methodology and Contributions

The authors present pFL-Bench, a benchmark designed to foster reproducible and comprehensive evaluations of pFL methods. The benchmark includes:

1. Dataset Variants: Over 10 datasets covering diverse application domains with uniform data partitioning and real-world heterogeneous settings.
2. Codebase: A modular and extendable codebase implementing over 20 pFL methods, allowing researchers to easily experiment and extend the benchmark.
3. Evaluation Framework: Systematic evaluations under controlled environments, analyzing factors such as generalization, fairness, system overhead, and convergence.

The benchmark is designed to support comparisons of pFL methods not only on the grounds of performance but also across different levels of federated learning generalizations, with assessments for both participating and new clients.

Results and Analysis

The paper details rigorous experiments assessing generalization and fairness, alongside resource efficiency metrics such as FLOPs and peak memory usage. Key findings include:

• Generalization Performance: Experiments revealed substantial variations in the generalization capabilities of existing pFL methods. While methods like Ditto and FedEM showed competitive performance in certain contexts, they exhibited limitations in scenarios involving new clients.
• Fairness and System Cost: An analysis of fairness metrics like distribution uniformity indicated disparities in client performance, underscoring the importance of improved fairness strategies in pFL designs. The benchmark also highlights the variable system costs associated with different pFL methods, notably in computational and communication overheads.

Theoretical and Practical Implications

The pFL-Bench framework highlights the trade-offs in designing pFL algorithms with diverse practical and theoretical implications:

• Algorithmic Improvements: The benchmark provides a foundation for developing more effective pFL methods by offering insights into the strengths and limitations of current approaches. The results emphasize the need for pFL algorithms that balance accuracy with computational and communication efficiency.
• Scalability in Real-world Scenarios: The inclusion of scenarios with heterogeneous device resources further reflects on the real-world applicability of pFL methods. The support for Differential Privacy introduces an essential consideration for privacy-preserving federated learning.

Future Directions

The benchmark opens several avenues for future research:

1. Enhanced Model Robustness: Designing models that maintain robustness across varying client distributions and connectivity challenges remains an open research question.
2. Trade-offs Between Personalization and Privacy: Exploring the interplay between personalization and privacy-preserving mechanisms such as Differential Privacy could lead to more secure and effective federated learning systems.
3. Benchmark Expansion: Continuous updates and community contributions to expand the benchmark's datasets and methods are encouraged to keep pace with the evolving landscape of federated learning research.

Concluding Thoughts

The "pFL-Bench" benchmark offers a thoughtful approach to tackling the challenges in evaluating pFL methods. The extensive dataset support and rigorous evaluation metrics provide a valuable tool for advancing research in personalized federated learning, helping bridge the gap between theoretical developments and practical applications in diverse real-world settings. The authors' commitment to maintaining and updating the benchmark underscores its potential long-term impact on the field.