mlsanityData to 7 Oct 2026

COALA: A Practical and Vision-Centric Federated Learning Platform

arXiv 2407.16560 ↗First posted 23 Jul 2024cs.CV cs.DCcited by 7

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Abstract (arXiv)

We present COALA, a vision-centric Federated Learning (FL) platform, and a suite of benchmarks for practical FL scenarios, which we categorize into three levels: task, data, and model. 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 tackle 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. Codes are open sourced at https://github.com/SonyResearch/COALA.

Key facts. Cited by 7 papers in the map (data through 7 Oct 2026). Builds on 69 of its 84 references that are in the map. First posted on arXiv 23 Jul 2024.

Builds on

69 of 84 references.

References · 69

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38 more
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50 most cited of 69 All 69 (JSON)

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 2; 2025: 4; 2026: 1.

7 papers.

  1. 2024FLoRA: Federated Fine-Tuning Large Language Models with Heterogeneous Low-Rank Adaptationscited by 144
  2. 2024Unleashing the Power of Continual Learning on Non-Centralized Devices: A Surveycited by 23
  3. 2025MedSegNet10: A Publicly Accessible Network Repository for Split Federated Medical Image Segmentationcited by 0
  4. 2025Hybrid Batch Normalisation: Resolving the Dilemma of Batch Normalisation in Federated Learningcited by 0
  5. 2025Closer to Reality: Practical Semi-Supervised Federated Learning for Foundation Model Adaptationcited by 0
  6. 2025FEDEXCHANGE: Bridging the Domain Gap in Federated Object Detection for Freecited by 0
  7. 2026Class-wise Contribution Estimation via Logit Maximization for Federated Learningcited by 0