Motley: Benchmarking Heterogeneity and Personalization in Federated Learning (2206.09262v6)

Abstract: Personalized federated learning considers learning models unique to each client in a heterogeneous network. The resulting client-specific models have been purported to improve metrics such as accuracy, fairness, and robustness in federated networks. However, despite a plethora of work in this area, it remains unclear: (1) which personalization techniques are most effective in various settings, and (2) how important personalization truly is for realistic federated applications. To better answer these questions, we propose Motley, a benchmark for personalized federated learning. Motley consists of a suite of cross-device and cross-silo federated datasets from varied problem domains, as well as thorough evaluation metrics for better understanding the possible impacts of personalization. We establish baselines on the benchmark by comparing a number of representative personalized federated learning methods. These initial results highlight strengths and weaknesses of existing approaches, and raise several open questions for the community. Motley aims to provide a reproducible means with which to advance developments in personalized and heterogeneity-aware federated learning, as well as the related areas of transfer learning, meta-learning, and multi-task learning.

• The paper introduces a comprehensive benchmark (PFL-Benchmark) to evaluate personalized federated learning across heterogeneous client datasets.
• The paper establishes baselines by comparing existing personalized FL methods using diverse cross-device and cross-silo datasets to reveal performance gaps.
• The paper’s analysis demonstrates that while personalization improves accuracy and fairness overall, fine-tuning may negatively impact 5-40% of clients in some settings.

Benchmarking Heterogeneity and Personalization in Federated Learning

This paper addresses the intricacies of personalized federated learning (FL) by introducing a comprehensive benchmark known as \textit{PFL-Benchmark}. In federated learning, heterogeneity is a predominant challenge, as each client may generate data according to unique distributions. The research emphasizes personalization, where models are tailored to each client, potentially improving metrics such as accuracy, fairness, and robustness. The benchmark proposed in this paper aims to clarify which techniques are most effective for personalization under different conditions and to assess the actual necessity of personalization in federated applications.

Core Contributions

1. Benchmark Design:

The authors offer a suite of cross-device and cross-silo datasets, encapsulating varied problem domains, providing a testing ground to evaluate personalization strategies. This is a valuable framework for understanding and measuring the impact of personalization on federated learning systems.

2. Baseline Establishment:

Existing personalized FL methods are evaluated across these datasets, drawing initial baselines. This highlights current strengths and weaknesses while prompting further exploration into open questions within the community.

3. Comprehensive Analysis:

The paper includes in-depth scrutiny of multiple datasets and proposes thorough evaluation metrics. This attention to detail aids in understanding the nuanced impacts of personalization and heterogeneity across different scenarios.

Numerical Insights

Key numerical results demonstrate that fine-tuning models (FedAvg+Fine-tuning) generally improved average per-client accuracy in cross-device settings for 3 out of 4 datasets. However, approximately 5-40% of clients could be negatively impacted post fine-tuning, depending on the dataset. In cross-silo settings, personalization consistently enhanced both accuracy and fairness, with methods like FedAvg+Fine-tuning and multi-task learning (MTL) showing superiority over standard techniques.

Practical and Theoretical Implications

Practical Considerations

• Hyperparameter Tuning: The challenges in optimizing fine-tuning due to varying local data distributions highlight a fundamental challenge in federated learning.
• Trade-offs: Personalization can lead to overfitting to current data distributions, impacting future generalization performance and increasing maintenance costs.
• Mode Collapse in Clustering: With HypCluster, mode collapse during training poses substantial challenges, impacting practical deployment and effectiveness.

Theoretical Considerations

• Evaluation Metrics: Mean accuracy alone is insufficient. Evaluating fairness, robustness, and communication efficiency should accompany personalization metrics.
• Method Development: The intricacies revealed by the benchmark suggest that more systematic approaches and novel strategies need to be developed for personalized FL, particularly regarding the tuning of strategies and hyperparameters at scale.

Future Directions in AI

This benchmark can inspire future work exploring privacy considerations, enriching datasets, and enhancing robustness in federated learning. The insights derived from this paper suggest that deploying effective personalized FL will require not only algorithmic refinements but also rethinking hardware and software infrastructures to accommodate more dynamic and responsive FL frameworks.

In summary, this benchmark opens new avenues for in-depth exploration of personalized federated learning, emphasizing the need for more nuanced evaluations and adaptive methods in coping with the inherent challenges of federated networks.