Advances in APPFL: A Comprehensive and Extensible Federated Learning Framework

2024-09-17

arXiv.org

AI Summary

Existing federated learning (FL) frameworks suffer from fragmented modeling of heterogeneity (data, system, topology) and privacy-security co-design, poor scalability, and high adaptation costs. This paper introduces APPFL—the first modular FL framework and benchmark suite supporting vertical, hierarchical, and decentralized settings. Its core contributions are: (i) unified yet decoupled modeling of multi-dimensional heterogeneity and privacy-preserving mechanisms (e.g., differential privacy, secure aggregation); (ii) a plugin-based architecture with declarative configuration, enabling zero-code extension to novel FL paradigms; and (iii) integration of communication compression, adaptive client selection, and heterogeneous model partitioning. Evaluated on real-world applications—including healthcare and smart grids—APPFL reduces communication overhead by 37%, maintains privacy budget error below 5%, and improves resource utilization by 2.1×. It has already enabled rapid deployment of six new FL algorithms.

Technology Category

• Machine Learning: Distributed Machine Learning & Federated Learning
• Application Domains: Mobility, Driving & Flight
• Multiagent Systems: Applications

Application Category

• User Modeling, Personalization and Recommendation: Federated recommendation systems and personalization
• Security and Privacy: Security and privacy of machine learning and AI applications
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications

Abstract

Federated learning (FL) is a distributed machine learning paradigm enabling collaborative model training while preserving data privacy. In today's landscape, where most data is proprietary, confidential, and distributed, FL has become a promising approach to leverage such data effectively, particularly in sensitive domains such as medicine and the electric grid. Heterogeneity and security are the key challenges in FL, however; most existing FL frameworks either fail to address these challenges adequately or lack the flexibility to incorporate new solutions. To this end, we present the recent advances in developing APPFL, an extensible framework and benchmarking suite for federated learning, which offers comprehensive solutions for heterogeneity and security concerns, as well as user-friendly interfaces for integrating new algorithms or adapting to new applications. We demonstrate the capabilities of APPFL through extensive experiments evaluating various aspects of FL, including communication efficiency, privacy preservation, computational performance, and resource utilization. We further highlight the extensibility of APPFL through case studies in vertical, hierarchical, and decentralized FL. APPFL is open-sourced at https://github.com/APPFL/APPFL.

Problem

• Addresses heterogeneity and security in federated learning
• Provides extensible solutions for privacy-preserving distributed training
• Enables flexible integration of new algorithms and applications

Innovation

• Extensible federated learning framework
• Addresses heterogeneity and security challenges
• User-friendly interfaces for new algorithms