CaBaFL: Asynchronous Federated Learning via Hierarchical Cache and Feature Balance
Not on the map. Why?
Abstract (arXiv)
Federated Learning (FL) as a promising distributed machine learning paradigm has been widely adopted in Artificial Intelligence of Things (AIoT) applications. However, the efficiency and inference capability of FL is seriously limited due to the presence of stragglers and data imbalance across massive AIoT devices, respectively. To address the above challenges, we present a novel asynchronous FL approach named CaBaFL, which includes a hierarchical Cache-based aggregation mechanism and a feature Balance-guided device selection strategy. CaBaFL maintains multiple intermediate models simultaneously for local training. The hierarchical cache-based aggregation mechanism enables each intermediate model to be trained on multiple devices to align the training time and mitigate the straggler issue. In specific, each intermediate model is stored in a low-level cache for local training and when it is trained by sufficient local devices, it will be stored in a high-level cache for aggregation. To address the problem of imbalanced data, the feature balance-guided device selection strategy in CaBaFL adopts the activation distribution as a metric, which enables each intermediate model to be trained across devices with totally balanced data distributions before aggregation. Experimental results show that compared with the state-of-the-art FL methods, CaBaFL achieves up to 9.26X training acceleration and 19.71% accuracy improvements.
Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 19 of its 41 references that are in the map. First posted on arXiv 19 Apr 2024.
Builds on
19 of 41 references.
References · 19
- 2019PyTorch: An Imperative Style, High-Performance Deep Learning Librarycited by 20,160
- 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,592
- 2019Advances and Open Problems in Federated Learningcited by 3,021
- 2018Federated Optimization in Heterogeneous Networkscited by 2,971
- 2019Measuring the Effects of Non-Identical Data Distribution for Federated Visual Classificationcited by 836
- 2018LEAF: A Benchmark for Federated Settingscited by 780
- 2019Robust and Communication-Efficient Federated Learning from Non-IID Datacited by 415
- 2021FedDG: Federated Domain Generalization on Medical Image Segmentation via Episodic Learning in Continuous Frequency Spacecited by 284
- 2019Asynchronous Federated Optimizationcited by 264
- 2019SAFA: a Semi-Asynchronous Protocol for Fast Federated Learning with Low Overheadcited by 82
- 2020FedCluster: Boosting the Convergence of Federated Learning via Cluster-Cyclingcited by 26
- 2022FedCross: Towards Accurate Federated Learning via Multi-Model Cross-Aggregationcited by 21
7 more
- 2022Scheduling and Aggregation Design for Asynchronous Federated Learning over Wireless Networkscited by 20
- 2023Towards Interpretable Federated Learningcited by 11
- 2022GitFL: Adaptive Asynchronous Federated Learning using Version Controlcited by 10
- 2018Distributed Learning over Unreliable Networkscited by 9
- 2023AdaptiveFL: Adaptive Heterogeneous Federated Learning for Resource-Constrained AIoT Systemscited by 9
- 2023Have Your Cake and Eat It Too: Toward Efficient and Accurate Split Federated Learningcited by 5
- 2023AdapterFL: Adaptive Heterogeneous Federated Learning for Resource-constrained Mobile Computing Systemscited by 4
Built on by
Citing papers per year: 2024: 1; 2025: 1.
2 papers.