DisPFL: Towards Communication-Efficient Personalized Federated Learning via Decentralized Sparse Training (2206.00187v1)

Abstract: Personalized federated learning is proposed to handle the data heterogeneity problem amongst clients by learning dedicated tailored local models for each user. However, existing works are often built in a centralized way, leading to high communication pressure and high vulnerability when a failure or an attack on the central server occurs. In this work, we propose a novel personalized federated learning framework in a decentralized (peer-to-peer) communication protocol named Dis-PFL, which employs personalized sparse masks to customize sparse local models on the edge. To further save the communication and computation cost, we propose a decentralized sparse training technique, which means that each local model in Dis-PFL only maintains a fixed number of active parameters throughout the whole local training and peer-to-peer communication process. Comprehensive experiments demonstrate that Dis-PFL significantly saves the communication bottleneck for the busiest node among all clients and, at the same time, achieves higher model accuracy with less computation cost and communication rounds. Furthermore, we demonstrate that our method can easily adapt to heterogeneous local clients with varying computation complexities and achieves better personalized performances.

• The paper introduces DisPFL, a decentralized sparse training framework that reduces communication and computation costs in personalized federated learning.
• It leverages personalized sparse masks to tailor models to diverse client data, effectively addressing data heterogeneity.
• Experiments validate improved model accuracy and resource efficiency compared to centralized federated learning approaches.

Communication-Efficient Personalized Federated Learning via Decentralized Sparse Training

The research article titled "DisPFL: Towards Communication-Efficient Personalized Federated Learning via Decentralized Sparse Training" introduces an innovative approach to enhancing personalized federated learning (PFL) through a decentralized architecture that capitalizes on the advantages of sparse training. This paper addresses key challenges in federated learning, particularly around data heterogeneity among clients and the communication bottlenecks associated with centralized architectures.

Overview

Federated Learning (FL) is a paradigm that allows multiple clients to collaboratively learn a shared global model without sharing local data. However, one significant challenge that arises in FL is data heterogeneity. This refers to the variance in data distributions across different clients, which can hinder the performance of global models if not properly managed. Personalized FL approaches are proposed to overcome this by creating individual models tailored to each client’s data.

Key Contributions

The authors propose a framework called Dis-PFL, which operates under a decentralized (peer-to-peer) communication protocol. This method eliminates the dependency on a central server, thereby enhancing robustness against server failure or attacks and reducing the communication load on any single node. Key contributions of this research include:

1. Decentralized Sparse Training: Dis-PFL introduces a decentralized sparse training model where each local client maintains a sparse neural network throughout the training and communication process. This is significant in reducing the communication and computation cost as well as accommodating different client capabilities.
2. Personalized Sparse Masks: Each client in Dis-PFL customizes its local model using a personalized sparse mask. This approach ensures that the local model is aligned with the client's data, further addressing the data heterogeneity issue in FL.
3. Experimental Validation: The paper showcases comprehensive experiments indicating that Dis-PFL significantly reduces communication costs, lowers computation costs, and achieves higher model accuracy compared to centralized FL approaches. Moreover, Dis-PFL handles heterogeneous client settings effectively, allowing clients with different computational resources to participate efficiently.
4. Generalization Analysis: The authors provide theoretical insights into the generalization capabilities of the sparse models, demonstrating that the models achieve a favorable trade-off between sparsity and performance.

Implications and Future Directions

The proposed Dis-PFL framework sets a precedent for how decentralized architectures can efficiently handle PFL’s inherent challenges, especially in communication-heavy environments. As the need for privacy-preserving and communication-efficient models grows, this research provides a solid foundation for future works exploring:

• Enhanced mask personalization techniques that can further improve model adaptiveness and accuracy.
• Integration with emerging technologies like edge computing to leverage distributed resources more effectively.
• Exploring dynamic sparsity ratios that can adapt in real-time based on network conditions or client capabilities.

Dis-PFL’s approach, which synergizes the strengths of decentralized networks with the efficiency of sparse training, presents promising avenues for developing robust, personalized, and resource-efficient federated learning systems suitable for a wide range of applications, from IoT devices to mobile computing.