Towards Personalized Federated Learning (2103.00710v3)

Abstract: In parallel with the rapid adoption of AI empowered by advances in AI research, there have been growing awareness and concerns of data privacy. Recent significant developments in the data regulation landscape have prompted a seismic shift in interest towards privacy-preserving AI. This has contributed to the popularity of Federated Learning (FL), the leading paradigm for the training of machine learning models on data silos in a privacy-preserving manner. In this survey, we explore the domain of Personalized FL (PFL) to address the fundamental challenges of FL on heterogeneous data, a universal characteristic inherent in all real-world datasets. We analyze the key motivations for PFL and present a unique taxonomy of PFL techniques categorized according to the key challenges and personalization strategies in PFL. We highlight their key ideas, challenges and opportunities and envision promising future trajectories of research towards new PFL architectural design, realistic PFL benchmarking, and trustworthy PFL approaches.

Towards Personalized Federated Learning

The paper "Towards Personalized Federated Learning" addresses the challenges posed by heterogeneous data in Federated Learning (FL) and proposes a systematic exploration of Personalized Federated Learning (PFL) strategies. The authors provide a comprehensive survey of PFL techniques, categorizing them based on key challenges and personalization strategies.

Overview and Motivation

Federated Learning has gained traction as a privacy-preserving AI paradigm, especially in data-sensitive domains such as healthcare. However, real-world datasets are inherently heterogeneous, presenting a fundamental challenge for FL. Existing FL methods predominantly focus on building a single global model, which may not perform well across all clients due to data distribution variances. PFL addresses this by tailoring models to individual clients, thereby enhancing their performance on heterogeneous data.

Taxonomy of PFL Approaches

The paper proposes a taxonomy of PFL techniques divided into two main strategies: Global Model Personalization and Learning Personalized Models.

1. Global Model Personalization: This strategy involves improving the global FL model through personalization techniques, allowing subsequent local adaptation for clients.
   • Data-based Approaches: These methods aim to reduce data heterogeneity through techniques like data augmentation and client selection, facilitating improved model training.
   • Model-based Approaches: These include regularization, meta-learning, and transfer learning, focusing on optimizing the global model for seamless personalization.
2. Learning Personalized Models: Directly addresses personalization by training individual client models while leveraging shared knowledge.
   • Architecture-based Approaches: Parameter decoupling and knowledge distillation allow flexible, personalized model architectures.
   • Similarity-based Approaches: Techniques like multi-task learning, model interpolation, and clustering exploit client similarities to tailor models effectively.

Evaluation and Benchmarking

The paper highlights the importance of realistic datasets and evaluation metrics for PFL. It critiques the widespread use of public datasets modified to simulate non-IID conditions, stressing the need for benchmarks that accurately reflect real-world federated data distributions. The authors advocate for comprehensive evaluation metrics encompassing model performance, system scalability, and trustworthy AI attributes.

Future Directions

The authors outline several promising research directions, including:

• Client Data Heterogeneity Analytics: Methods to analyze client data distributions in a privacy-preserving manner.
• Aggregation Procedure Innovations: Beyond simple model averaging, exploring specialized aggregation techniques.
• Spatial and Temporal Adaptability: Enhancing resilience to client dynamics and non-stationary data.

Conclusion

This paper contributes significantly to the understanding of PFL by presenting a well-structured taxonomy and identifying gaps in current research. The authors emphasize the balance between global optimization and local personalization, paving the way for advanced adaptive learning systems. As PFL continues to evolve, addressing the outlined challenges will be crucial for its deployment in diverse, real-world applications.