Hierarchical Federated Learning through LAN-WAN Orchestration
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Abstract (arXiv)
Federated learning (FL) was designed to enable mobile phones to collaboratively learn a global model without uploading their private data to a cloud server. However, exiting FL protocols has a critical communication bottleneck in a federated network coupled with privacy concerns, usually powered by a wide-area network (WAN). Such a WAN-driven FL design leads to significantly high cost and much slower model convergence. In this work, we propose an efficient FL protocol, which involves a hierarchical aggregation mechanism in the local-area network (LAN) due to its abundant bandwidth and almost negligible monetary cost than WAN. Our proposed FL can accelerate the learning process and reduce the monetary cost with frequent local aggregation in the same LAN and infrequent global aggregation on a cloud across WAN. We further design a concrete FL platform, namely LanFL, that incorporates several key techniques to handle those challenges introduced by LAN: cloud-device aggregation architecture, intra-LAN peer-to-peer (p2p) topology generation, inter-LAN bandwidth capacity heterogeneity. We evaluate LanFL on 2 typical Non-IID datasets, which reveals that LanFL can significantly accelerate FL training (1.5x-6.0x), save WAN traffic (18.3x-75.6x), and reduce monetary cost (3.8x-27.2x) while preserving the model accuracy.
Key facts. Cited by 13 papers in the map (data through 7 Oct 2026). First posted on arXiv 22 Oct 2020.
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Citing papers per year: 2021: 2; 2022: 3; 2023: 7; 2024: 1.
13 papers.
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- 2021From Distributed Machine Learning to Federated Learning: A Surveycited by 65
- 2023Topology-aware Federated Learning in Edge Computing: A Comprehensive Surveycited by 23
- 2022Scalable and Low-Latency Federated Learning with Cooperative Mobile Edge Networkingcited by 18
- 2023HiFlash: Communication-Efficient Hierarchical Federated Learning with Adaptive Staleness Control and Heterogeneity-aware Client-Edge Associationcited by 17
- 2023DYNAMITE: Dynamic Interplay of Mini-Batch Size and Aggregation Frequency for Federated Learning with Static and Streaming Datasetcited by 14
- 2023Adaptive Federated Pruning in Hierarchical Wireless Networkscited by 10
- 2024Fast-Convergent and Communication-Alleviated Heterogeneous Hierarchical Federated Learning in Autonomous Drivingcited by 8
- 2023Communication Resources Constrained Hierarchical Federated Learning for End-to-End Autonomous Drivingcited by 7
- 2023Data-Aware Gradient Compression for FL in Communication-Constrained Mobile Computingcited by 5
- 2022Asynchronous Hierarchical Federated Learningcited by 4
- 2022Hierarchical Federated Learning with Privacycited by 4
- 2021Secure Multi-Party Computation based Privacy Preserving Data Analysis in Healthcare IoT Systemscited by 3
- 2024Fast-Convergent and Communication-Alleviated Heterogeneous Hierarchical Federated Learning in Autonomous Driving29 Sep 2024
- 2023Data-Aware Gradient Compression for FL in Communication-Constrained Mobile Computing13 Nov 2023
- 2023DYNAMITE: Dynamic Interplay of Mini-Batch Size and Aggregation Frequency for Federated Learning with Static and Streaming Dataset20 Oct 2023
- 2023User Assignment and Resource Allocation for Hierarchical Federated Learning over Wireless Networks17 Sep 2023
- 2023Communication Resources Constrained Hierarchical Federated Learning for End-to-End Autonomous Driving28 Jun 2023
- 2023Adaptive Federated Pruning in Hierarchical Wireless Networks15 May 2023
- 2023Topology-aware Federated Learning in Edge Computing: A Comprehensive Survey6 Feb 2023
- 2023HiFlash: Communication-Efficient Hierarchical Federated Learning with Adaptive Staleness Control and Heterogeneity-aware Client-Edge Association16 Jan 2023
- 2022Hierarchical Federated Learning with Privacy10 Jun 2022
- 2022Asynchronous Hierarchical Federated Learning31 May 2022
- 2022Scalable and Low-Latency Federated Learning with Cooperative Mobile Edge Networking25 May 2022
- 2021Secure Multi-Party Computation based Privacy Preserving Data Analysis in Healthcare IoT Systems29 Sep 2021
1 more
- 2021From Distributed Machine Learning to Federated Learning: A Surveycited by 65
- 2023Topology-aware Federated Learning in Edge Computing: A Comprehensive Surveycited by 23
- 2022Scalable and Low-Latency Federated Learning with Cooperative Mobile Edge Networkingcited by 18
- 2023HiFlash: Communication-Efficient Hierarchical Federated Learning with Adaptive Staleness Control and Heterogeneity-aware Client-Edge Associationcited by 17
- 2023DYNAMITE: Dynamic Interplay of Mini-Batch Size and Aggregation Frequency for Federated Learning with Static and Streaming Datasetcited by 14
- 2023Adaptive Federated Pruning in Hierarchical Wireless Networkscited by 10
- 2024Fast-Convergent and Communication-Alleviated Heterogeneous Hierarchical Federated Learning in Autonomous Drivingcited by 8
- 2023Data-Aware Gradient Compression for FL in Communication-Constrained Mobile Computingcited by 5
- 2022Asynchronous Hierarchical Federated Learningcited by 4
- 2022Hierarchical Federated Learning with Privacycited by 4
- 2021Secure Multi-Party Computation based Privacy Preserving Data Analysis in Healthcare IoT Systemscited by 3
- 2023User Assignment and Resource Allocation for Hierarchical Federated Learning over Wireless Networkscited by 1
- 2024Fast-Convergent and Communication-Alleviated Heterogeneous Hierarchical Federated Learning in Autonomous Driving29 Sep 2024
- 2023Data-Aware Gradient Compression for FL in Communication-Constrained Mobile Computing13 Nov 2023
- 2023DYNAMITE: Dynamic Interplay of Mini-Batch Size and Aggregation Frequency for Federated Learning with Static and Streaming Dataset20 Oct 2023
- 2023User Assignment and Resource Allocation for Hierarchical Federated Learning over Wireless Networks17 Sep 2023
- 2023Adaptive Federated Pruning in Hierarchical Wireless Networks15 May 2023
- 2023Topology-aware Federated Learning in Edge Computing: A Comprehensive Survey6 Feb 2023
- 2023HiFlash: Communication-Efficient Hierarchical Federated Learning with Adaptive Staleness Control and Heterogeneity-aware Client-Edge Association16 Jan 2023
- 2022Hierarchical Federated Learning with Privacy10 Jun 2022
- 2022Asynchronous Hierarchical Federated Learning31 May 2022
- 2022Scalable and Low-Latency Federated Learning with Cooperative Mobile Edge Networking25 May 2022
- 2021Secure Multi-Party Computation based Privacy Preserving Data Analysis in Healthcare IoT Systems29 Sep 2021
- 2021From Distributed Machine Learning to Federated Learning: A Survey29 Apr 2021