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Asynchronous Federated Optimization

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arxiv 1903.03934 v5 pith:TNYNQEQX submitted 2019-03-10 cs.DC cs.LG

Cong Xie , Sanmi Koyejo , Indranil Gupta This is my paper · ORCID
classification cs.DCcs.LG
keywords federatedalgorithmasynchronousoptimizationproposedapplicationsapproachconvergence
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Federated learning enables training on a massive number of edge devices. To improve flexibility and scalability, we propose a new asynchronous federated optimization algorithm. We prove that the proposed approach has near-linear convergence to a global optimum, for both strongly convex and a restricted family of non-convex problems. Empirical results show that the proposed algorithm converges quickly and tolerates staleness in various applications.

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Cited by 14 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Robust Federated Learning Under Real-World Client Churn

    cs.LG 2026-07 conditional novelty 6.0 of 10

    FeLiX reduces wall-clock time-to-target accuracy in federated learning by up to 2.37x using lightweight availability tiers, fresh-utility client selection, and informativeness-aware aggregation without requiring oracu...

  2. FedSA-GCL: A Semi-Asynchronous Federated Graph Learning Framework with Personalized Aggregation and Cluster-Aware Broadcasting

    cs.LG 2025-07 conditional novelty 6.0 of 10

    A semi-asynchronous federated graph learning framework with soft-label clustering, staleness-weighted aggregation, and cluster broadcasting reports higher accuracy and faster convergence than ten baselines.

  3. Optimal Batch-Size Control for Low-Latency Federated Learning with Device Heterogeneity

    cs.LG 2025-07 conditional novelty 6.0 of 10

    Wireless federated learning can cut end-to-end training time by choosing per-device batch sizes with a closed-form rule that balances convergence rounds against per-round latency.

  4. Flotilla: A scalable, modular and resilient federated learning framework for heterogeneous resources

    cs.DC 2025-07 conditional novelty 6.0 of 10

    Flotilla is a modular, resilient federated learning framework that runs on heterogeneous edge devices, supports sync and async strategies, and scales to 1000+ clients with low overhead.

  5. AFBS:Buffer Gradient Selection in Semi-asynchronous Federated Learning

    cs.LG 2025-06 conditional novelty 6.0 of 10

    AFBS scores buffered gradients by staleness and dataset size, discards low-value ones, and clusters clients through random-projection-encrypted label distributions before aggregation in semi-asynchronous federated learning.

  6. HALoS: Hierarchical Asynchronous Local SGD over Slow Networks for Geo-Distributed Large Language Model Training

    cs.LG 2025-06 conditional novelty 6.0 of 10

    A hierarchical asynchronous local SGD method with regional parameter servers and global model merging is claimed to train small LLMs up to 7.5x faster than DiLoCo in simulated geo-distributed settings.

  7. FedCostAware: Enabling Cost-Aware Federated Learning on the Cloud

    cs.DC 2025-05 conditional novelty 6.0 of 10

    FedCostAware reports cutting cloud costs for synchronous federated learning by up to 72 percent using smarter spot-instance lifecycle management.

  8. Efficient Federated Learning with Timely Update Dissemination

    cs.DC 2025-07 conditional novelty 5.0 of 10

    FedASMU and FedSSMU improve federated learning accuracy and speed by dynamically disseminating fresh global models to devices during local training, using server-side and device-side adaptive weighting.

  9. Air-FedGA: A Grouping Asynchronous Federated Learning Mechanism Exploiting Over-the-air Computation

    cs.DC 2025-07 conditional novelty 5.0 of 10

    Grouping workers so over-the-air aggregation happens inside groups while groups update asynchronously cuts simulated federated learning training time by 30-72%.

  10. Decentralized Pliable Index Coding For Federated Learning In Intelligent Transportation Systems

    cs.IT 2025-07 reject novelty 5.0 of 10

    Pliable index coding with consecutive side-information is used to shuffle synthetic data among federated learning nodes, reducing transmissions and improving convergence in ITS simulations.

  11. Multimodal Federated Learning: A Survey through the Lens of Different FL Paradigms

    cs.LG 2025-05 conditional novelty 5.0 of 10

    A paradigm-based taxonomy of multimodal federated learning that assigns each branch a headline challenge: modality heterogeneity (horizontal), privacy leakage (vertical), and efficiency (hybrid).

  12. Efficient Asynchronous Federated Evaluation with Strategy Similarity Awareness for Intent-Based Networking in Industrial Internet of Things

    cs.NI 2025-11 conditional novelty 4.0 of 10

    SSAFL uses historical strategy similarity plus resource availability to select IIoT nodes and trigger asynchronous FL uploads, improving accuracy and cutting communication cost for intent-based policy verification in ...

  13. FedGA: A Fair Federated Learning Framework Based on the Gini Coefficient

    cs.LG 2025-07 conditional novelty 4.0 of 10

    FedGA, a fairness-aware federated learning method, uses the Gini coefficient to trigger delayed reweighting of aggregation toward low-accuracy clients, improving fairness metrics while maintaining accuracy on three datasets.

  14. Integrating Asynchronous AdaBoost into Federated Learning: Five Real World Applications

    cs.LG 2025-06 reject novelty 2.0 of 10

    An application-level analysis claiming broad efficiency gains for an enhanced asynchronous AdaBoost, but all quantitative gains are extrapolated from the authors' prior work without new experiments.

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