mlsanityData to 7 Oct 2026

GitFL: Adaptive Asynchronous Federated Learning using Version Control

arXiv 2211.12049 ↗First posted 22 Nov 2022cs.LGcited by 10

Not on the map. Why?

Abstract (arXiv)

As a promising distributed machine learning paradigm that enables collaborative training without compromising data privacy, Federated Learning (FL) has been increasingly used in AIoT (Artificial Intelligence of Things) design. However, due to the lack of efficient management of straggling devices, existing FL methods greatly suffer from the problems of low inference accuracy and long training time. Things become even worse when taking various uncertain factors (e.g., network delays, performance variances caused by process variation) existing in AIoT scenarios into account. To address this issue, this paper proposes a novel asynchronous FL framework named GitFL, whose implementation is inspired by the famous version control system Git. Unlike traditional FL, the cloud server of GitFL maintains a master model (i.e., the global model) together with a set of branch models indicating the trained local models committed by selected devices, where the master model is updated based on both all the pushed branch models and their version information, and only the branch models after the pull operation are dispatched to devices. By using our proposed Reinforcement Learning (RL)-based device selection mechanism, a pulled branch model with an older version will be more likely to be dispatched to a faster and less frequently selected device for the next round of local training. In this way, GitFL enables both effective control of model staleness and adaptive load balance of versioned models among straggling devices, thus avoiding the performance deterioration. Comprehensive experimental results on well-known models and datasets show that, compared with state-of-the-art asynchronous FL methods, GitFL can achieve up to 2.64X training acceleration and 7.88% inference accuracy improvements in various uncertain scenarios.

Key facts. Cited by 10 papers in the map (data through 7 Oct 2026). Builds on 15 of its 42 references that are in the map. First posted on arXiv 22 Nov 2022.

Builds on

15 of 42 references.

References · 15

  1. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,592
  2. 2019Advances and Open Problems in Federated Learningcited by 3,021
  3. 2018Federated Optimization in Heterogeneous Networkscited by 2,971
  4. 2017Curiosity-driven Exploration by Self-supervised Predictioncited by 1,173
  5. 2018LEAF: A Benchmark for Federated Settingscited by 780
  6. 2016Unifying Count-Based Exploration and Intrinsic Motivationcited by 632
  7. 2021Federated Learning Based on Dynamic Regularizationcited by 555
  8. 2019Asynchronous Federated Optimizationcited by 264
  9. 2019Model Pruning Enables Efficient Federated Learning on Edge Devicescited by 219
  10. 2021Asynchronous Federated Learning on Heterogeneous Devices: A Surveycited by 93
  11. 2019SAFA: a Semi-Asynchronous Protocol for Fast Federated Learning with Low Overheadcited by 82
  12. 2020Adaptive Federated Learning and Digital Twin for Industrial Internet of Thingscited by 27
3 more
  1. 2022Federated Contrastive Learning for Dermatological Disease Diagnosis via On-device Learningcited by 21
  2. 2021HADFL: Heterogeneity-aware Decentralized Federated Learning Frameworkcited by 5
  3. 2021Lithography Hotspot Detection via Heterogeneous Federated Learning with Local Adaptationcited by 4

Built on by

Cited by year

Citing papers per year: 2023: 5; 2024: 3; 2025: 2.

10 papers.

  1. 2023Is Aggregation the Only Choice? Federated Learning via Layer-wise Model Recombinationcited by 13
  2. 2023Towards Interpretable Federated Learningcited by 11
  3. 2023AdaptiveFL: Adaptive Heterogeneous Federated Learning for Resource-Constrained AIoT Systemscited by 9
  4. 2023Have Your Cake and Eat It Too: Toward Efficient and Accurate Split Federated Learningcited by 5
  5. 2023AdapterFL: Adaptive Heterogeneous Federated Learning for Resource-constrained Mobile Computing Systemscited by 4
  6. 2025Federated Deconfounding and Debiasing Learning for Out-of-Distribution Generalizationcited by 4
  7. 2024CaBaFL: Asynchronous Federated Learning via Hierarchical Cache and Feature Balancecited by 2
  8. 2024KoReA-SFL: Knowledge Replay-based Split Federated Learning Against Catastrophic Forgettingcited by 0
  9. 2024FedQP: Towards Accurate Federated Learning using Quadratic Programming Guided Mutationcited by 0
  10. 2025Gradients as an Action: Towards Communication-Efficient Federated Recommender Systems via Adaptive Action Sharingcited by 0