Logo
ScienceStack
Table of Contents
Fetching ...
Sign In

Advances in APPFL: A Comprehensive and Extensible Federated Learning Framework

Zilinghan Li, Shilan He, Ze Yang, Minseok Ryu, Kibaek Kim, Ravi Madduri

TL;DR

The paper addresses the core challenges of heterogeneity and security in federated learning by introducing APPFL, an extensible, open-source framework and benchmarking suite. It presents a modular architecture with server/client agents, a versatile communication stack, multiple scheduling and aggregation strategies, and built-in privacy/authentication mechanisms. Through comprehensive performance evaluations and diverse case studies (vertical, hierarchical, and decentralized FL), the work demonstrates APPFL’s ability to adapt to a wide range of FL scenarios and to benchmark key components such as communication, compression, and privacy options. The framework’s extensibility and practical emphasis on secure, scalable FL offer a significant contribution to both FL research and applications across sensitive domains.

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.

Advances in APPFL: A Comprehensive and Extensible Federated Learning Framework

TL;DR

The paper addresses the core challenges of heterogeneity and security in federated learning by introducing APPFL, an extensible, open-source framework and benchmarking suite. It presents a modular architecture with server/client agents, a versatile communication stack, multiple scheduling and aggregation strategies, and built-in privacy/authentication mechanisms. Through comprehensive performance evaluations and diverse case studies (vertical, hierarchical, and decentralized FL), the work demonstrates APPFL’s ability to adapt to a wide range of FL scenarios and to benchmark key components such as communication, compression, and privacy options. The framework’s extensibility and practical emphasis on secure, scalable FL offer a significant contribution to both FL research and applications across sensitive domains.

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.
Paper Structure (20 sections, 12 figures, 3 tables)

This paper contains 20 sections, 12 figures, 3 tables.

Table of Contents

  1. Introduction
  2. Background and Related Work
  3. Heterogeneity in Federated Learning
  4. Attacks and Security Concerns in Federated Learning
  5. Existing Federated Learning Frameworks
  6. Framework Architecture and Implementation
  7. FL Experiment Configuration
  8. Communication Stack
  9. Server Scheduling and Aggregation
  10. Privacy Preservation and Authentication
  11. Performance Evaluation
  12. Communication Efficiency
  13. Compression Efficiency
  14. Privacy Preservation
  15. Addressing Heterogeneous Clients
  16. ...and 5 more sections

Figures (12)

  • Figure 1: Overview of the Appfl framework's new software architecture design. Server agent and client agent act on behalf of the FL server and client, respectively, to fulfill various tasks for FL experiments. Communicator exchanges task control signals and model parameters between the server and client.
  • Figure 2: Running one local training and global aggregation iteration using (a) client-driven and (b) server-driven communication protocols.
  • Figure 3: Scheduling of the aggregation for client local models under three schedulers with different synchronicity settings.
  • Figure 4: Login flow for the Globus authenticator.
  • Figure 5: Efficiency comparison of communication and data transfer protocols: Average two-way communication time per global epoch and the corresponding standard deviation as the number of clients increases exponentially across various models.
  • ...and 7 more figures