FLEdge: Benchmarking Federated Machine Learning Applications in Edge Computing Systems
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Abstract (arXiv)
Federated Learning (FL) has become a viable technique for realizing privacy-enhancing distributed deep learning on the network edge. Heterogeneous hardware, unreliable client devices, and energy constraints often characterize edge computing systems. In this paper, we propose FLEdge, which complements existing FL benchmarks by enabling a systematic evaluation of client capabilities. We focus on computational and communication bottlenecks, client behavior, and data security implications. Our experiments with models varying from 14K to 80M trainable parameters are carried out on dedicated hardware with emulated network characteristics and client behavior. We find that state-of-the-art embedded hardware has significant memory bottlenecks, leading to 4x longer processing times than on modern data center GPUs.
Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). Builds on 28 of its 67 references that are in the map. First posted on arXiv 8 Jun 2023.
Builds on
28 of 67 references.
References · 28
- 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,669
- 2017MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applicationscited by 3,845
- 2022Scaling Instruction-Finetuned Language Modelscited by 3,024
- 2018Federated Optimization in Heterogeneous Networkscited by 2,987
- 2016Federated Learning: Strategies for Improving Communication Efficiencycited by 1,475
- 2020Adaptive Federated Optimizationcited by 1,046
- 2018LEAF: A Benchmark for Federated Settingscited by 792
- 2019SAMSum Corpus: A Human-annotated Dialogue Dataset for Abstractive Summarizationcited by 535
- 2020Flower: A Friendly Federated Learning Research Frameworkcited by 455
- 2019Robust and Communication-Efficient Federated Learning from Non-IID Datacited by 419
- 2019Fair Resource Allocation in Federated Learningcited by 394
- 2020FedML: A Research Library and Benchmark for Federated Machine Learningcited by 300
16 more
- 2019Differentially Private Learning with Adaptive Clippingcited by 220
- 2021FedKD: Communication Efficient Federated Learning via Knowledge Distillationcited by 187
- 2021FedScale: Benchmarking Model and System Performance of Federated Learning at Scalecited by 136
- 2021Linear Convergence in Federated Learning: Tackling Client Heterogeneity and Sparse Gradientscited by 123
- 2020Client Selection and Bandwidth Allocation in Wireless Federated Learning Networks: A Long-Term Perspectivecited by 103
- 2018In-Edge AI: Intelligentizing Mobile Edge Computing, Caching and Communication by Federated Learningcited by 80
- 2021Federated Reconstruction: Partially Local Federated Learningcited by 79
- 2021On Large-Cohort Training for Federated Learningcited by 74
- 2021Compute and Energy Consumption Trends in Deep Learning Inferencecited by 61
- 2021ProgFed: Effective, Communication, and Computation Efficient Federated Learning by Progressive Trainingcited by 27
- 2016Challenges and Opportunities in Edge Computingcited by 26
- 2023FS-Real: Towards Real-World Cross-Device Federated Learningcited by 22
- 2023FLINT: A Platform for Federated Learning Integrationcited by 17
- 2022FedHPO-B: A Benchmark Suite for Federated Hyperparameter Optimizationcited by 6
- 2021ERRANT: Realistic Emulation of Radio Access Networkscited by 2
- 2022Scheduling IoT Applications in Edge and Fog Computing Environments: A Taxonomy and Future Directionscited by 1
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Citing papers per year: 2024: 1; 2025: 1; 2026: 1.
3 papers.