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FedAT: A High-Performance and Communication-Efficient Federated Learning System with Asynchronous Tiers

arXiv 2010.05958 ↗First posted 12 Oct 2020cs.DC cs.LG cs.NIcited by 64

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Abstract (arXiv)

Federated learning (FL) involves training a model over massive distributed devices, while keeping the training data localized. This form of collaborative learning exposes new tradeoffs among model convergence speed, model accuracy, balance across clients, and communication cost, with new challenges including: (1) straggler problem, where the clients lag due to data or (computing and network) resource heterogeneity, and (2) communication bottleneck, where a large number of clients communicate their local updates to a central server and bottleneck the server. Many existing FL methods focus on optimizing along only one dimension of the tradeoff space. Existing solutions use asynchronous model updating or tiering-based synchronous mechanisms to tackle the straggler problem. However, the asynchronous methods can easily create a network communication bottleneck, while tiering may introduce biases as tiering favors faster tiers with shorter response latencies. To address these issues, we present FedAT, a novel Federated learning method with Asynchronous Tiers under Non-i.i.d. data. FedAT synergistically combines synchronous intra-tier training and asynchronous cross-tier training. By bridging the synchronous and asynchronous training through tiering, FedAT minimizes the straggler effect with improved convergence speed and test accuracy. FedAT uses a straggler-aware, weighted aggregation heuristic to steer and balance the training for further accuracy improvement. FedAT compresses the uplink and downlink communications using an efficient, polyline-encoding-based compression algorithm, therefore minimizing the communication cost. Results show that FedAT improves the prediction performance by up to 21.09%, and reduces the communication cost by up to 8.5x, compared to state-of-the-art FL methods.

Key facts. Cited by 64 papers in the map (data through 7 Oct 2026). First posted on arXiv 12 Oct 2020.

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Citing papers per year: 2021: 2; 2022: 13; 2023: 18; 2024: 18; 2025: 8; 2026: 5.

64 papers.

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  1. 2022Asynchronous SGD Beats Minibatch SGD Under Arbitrary Delayscited by 61
  2. 2022FederatedScope: A Flexible Federated Learning Platform for Heterogeneitycited by 53
  3. 2023Federated Learning for Computationally-Constrained Heterogeneous Devices: A Surveycited by 40
  4. 2022FedHiSyn: A Hierarchical Synchronous Federated Learning Framework for Resource and Data Heterogeneitycited by 20
  5. 2022Scheduling and Aggregation Design for Asynchronous Federated Learning over Wireless Networkscited by 20
  6. 2023MP-FedCL: Multiprototype Federated Contrastive Learning for Edge Intelligencecited by 20
  7. 2023HiFlash: Communication-Efficient Hierarchical Federated Learning with Adaptive Staleness Control and Heterogeneity-aware Client-Edge Associationcited by 17
  8. 2023FedVS: Straggler-Resilient and Privacy-Preserving Vertical Federated Learning for Split Modelscited by 17
  9. 2023FLuID: Mitigating Stragglers in Federated Learning using Invariant Dropoutcited by 16
  10. 2023FedCompass: Efficient Cross-Silo Federated Learning on Heterogeneous Client Devices using a Computing Power Aware Schedulercited by 13
  11. 2021SPATL: Salient Parameter Aggregation and Transfer Learning for Heterogeneous Clients in Federated Learningcited by 11
  12. 2021FedLGA: Towards System-Heterogeneity of Federated Learning via Local Gradient Approximationcited by 10
38 more
  1. 2022Time-triggered Federated Learning over Wireless Networkscited by 10
  2. 2023Making Batch Normalization Great in Federated Deep Learningcited by 8
  3. 2024A Comprehensive Survey of Federated Transfer Learning: Challenges, Methods and Applicationscited by 8
  4. 2022FedDRL: Deep Reinforcement Learning-based Adaptive Aggregation for Non-IID Data in Federated Learningcited by 7
  5. 2022Accelerating Asynchronous Federated Learning Convergence via Opportunistic Mobile Relayingcited by 6
  6. 2024Responsible Artificial Intelligence: A Structured Literature Reviewcited by 6
  7. 2022Olive Branch Learning: A Topology-Aware Federated Learning Framework for Space-Air-Ground Integrated Networkcited by 5
  8. 2023Multi-Carrier NOMA-Empowered Wireless Federated Learning with Optimal Power and Bandwidth Allocationcited by 5
  9. 2023CADIS: Handling Cluster-skewed Non-IID Data in Federated Learning with Clustered Aggregation and Knowledge DIStilled Regularizationcited by 5
  10. 2023Have Your Cake and Eat It Too: Toward Efficient and Accurate Split Federated Learningcited by 5
  11. 2025ZenFlow: Enabling Stall-Free Offloading Training via Asynchronous Updatescited by 5
  12. 2022Enabling All In-Edge Deep Learning: A Literature Reviewcited by 4
  13. 2024Stragglers-Aware Low-Latency Synchronous Federated Learning via Layer-Wise Model Updatescited by 4
  14. 2024Logit Calibration and Feature Contrast for Robust Federated Learning on Non-IID Datacited by 4
  15. 2022Distributed Graph Neural Network Training with Periodic Stale Representation Synchronizationcited by 3
  16. 2022Latency Aware Semi-synchronous Client Selection and Model Aggregation for Wireless Federated Learningcited by 3
  17. 2023Speed Up Federated Learning in Heterogeneous Environment: A Dynamic Tiering Approachcited by 3
  18. 2024Momentum Approximation in Asynchronous Private Federated Learningcited by 3
  19. 2024Low-Energy On-Device Personalization for MCUscited by 3
  20. 2024Joint Model Pruning and Resource Allocation for Wireless Time-triggered Federated Learningcited by 3
  21. 2024Unity is Power: Semi-Asynchronous Collaborative Training of Large-Scale Models with Structured Pruning in Resource-Limited Clientscited by 3
  22. 2024Federated Learning in Chemical Engineering: A Tutorial on a Framework for Privacy-Preserving Collaboration Across Distributed Data Sourcescited by 3
  23. 2025Fairness in Federated Learning: Trends, Challenges, and Opportunitiescited by 3
  24. 2022FedLesScan: Mitigating Stragglers in Serverless Federated Learningcited by 2
  25. 2023FLIPS: Federated Learning using Intelligent Participant Selectioncited by 2
  26. 2024FedCore: Straggler-Free Federated Learning with Distributed Coresetscited by 2
  27. 2024Communication-Efficient Training Workload Balancing for Decentralized Multi-Agent Learningcited by 2
  28. 2024Dataset Distillation-based Hybrid Federated Learning on Non-IID Datacited by 2
  29. 2024Federated Hybrid Training and Self-Adversarial Distillation: Towards Robust Edge Networkscited by 2
  30. 2025FedGA: A Fair Federated Learning Framework Based on the Gini Coefficientcited by 2
  31. 2023FedIN: Federated Intermediate Layers Learning for Model Heterogeneitycited by 1
  32. 2023Joint Client Assignment and UAV Route Planning for Indirect-Communication Federated Learningcited by 1
  33. 2023Federated Learning in IoT: a Survey from a Resource-Constrained Perspectivecited by 1
  34. 2024A Joint Approach to Local Updating and Gradient Compression for Efficient Asynchronous Federated Learningcited by 1
  35. 2024DynamicFL: Federated Learning with Dynamic Communication Resource Allocationcited by 1
  36. 2025TT-Prune: Joint Model Pruning and Resource Allocation for Communication-efficient Time-triggered Federated Learningcited by 1
  37. 2026Scalable Cross-Facility Federated Learning for Scientific Foundation Models on Multiple Supercomputerscited by 1
  38. 2022Reducing Impacts of System Heterogeneity in Federated Learning using Weight Update Magnitudescited by 0

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