Advances in APPFL: A Comprehensive and Extensible Federated Learning Framework

TL;DR

This paper introduces APPFL, an extensible federated learning framework that addresses heterogeneity and security challenges, providing a flexible platform for diverse FL applications and extensive benchmarking.

Contribution

The paper presents APPFL, a new open-source federated learning framework with comprehensive solutions for heterogeneity, security, and extensibility, along with benchmarking tools and case studies.

Findings

APPFL effectively handles heterogeneity and security in FL.

Extensive experiments demonstrate communication efficiency and privacy preservation.

Case studies validate APPFL's flexibility in various FL scenarios.

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 fully open-sourced on GitHub at https://github.com/APPFL/APPFL.