Authors

Venue

Date

Share

COALA: A Practical and Vision-Centric Federated Learning Platform

Weiming Zhuang

Jian Xu

Chen Chen

Jingtao Li

Lingjuan Lyu

ICML-24

2024

Abstract

We present COALA, a vision-centric Federated Learning (FL) platform, and a suite of benchmarks for practical FL scenarios, which we categorize as task, data, and model levels. At the task level, COALA extends support from simple classification to 15 computer vision tasks, including object detection, segmentation, pose estimation, and more. It also facilitates federated multiple-task learning, allowing clients to train on multiple tasks simultaneously. At the data level, COALA goes beyond supervised FL to benchmark both semi-supervised FL and unsupervised FL. It also benchmarks feature distribution shifts other than commonly considered label distribution shifts. In addition to dealing with static data, it supports federated continual learning for continuously changing data in real-world scenarios. At the model level, COALA benchmarks FL with split models and different models in different clients. COALA platform offers three degrees of customization for these practical FL scenarios, including configuration customization, components customization, and workflow customization. We conduct systematic benchmarking experiments for the practical FL scenarios and highlight potential opportunities for further advancements in FL.

Related Publications

Self-Comparison for Dataset-Level Membership Inference in Large (Vision-)Language Model

WWW, 2025
Jie Ren, Kangrui Chen, Chen Chen, Vikash Sehwag, Yue Xing, Jiliang Tang, Lingjuan Lyu

Large Language Models (LLMs) and Vision-Language Models (VLMs) have made significant advancements in a wide range of natural language processing and vision-language tasks. Access to large web-scale datasets has been a key factor in their success. However, concerns have been …

Exploit Gradient Skewness to Circumvent Byzantine Defenses for Federated Learning

AAAI, 2025
Yuchen Liu*, Chen Chen, Lingjuan Lyu, Yaochu Jin, Gang Chen*

Federated Learning (FL) is notorious for its vulnerability to Byzantine attacks. Most current Byzantine defenses share a common inductive bias: among all the gradients, the densely distributed ones are more likely to be honest. However, such a bias is a poison to Byzantine r…

FLoRA: Federated Fine-Tuning Large Language Models with Heterogeneous Low- Rank Adaptations

NeurIPS, 2024
Lingjuan Lyu, Ziyao Wang, Zheyu Shen, Yexiao He, Guoheng Sun, Hongyi Wang, Ang Li

The rapid development of Large Language Models (LLMs) has been pivotal in advancing AI, with pre-trained LLMs being adaptable to diverse downstream tasks through fine-tuning. Federated learning (FL) further enhances fine-tuning in a privacy-aware manner by utilizing clients'…

  • HOME
  • Publications
  • COALA: A Practical and Vision-Centric Federated Learning Platform

JOIN US

Shape the Future of AI with Sony AI

We want to hear from those of you who have a strong desire
to shape the future of AI.