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Delay-Aware Hierarchical Federated Learning
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DOI：10.1109/TCCN.2023.3329024.png)
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摘要
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Federated learning has gained popularity as a means of training models distributed across the wireless edge. The paper introduces delay-aware hierarchical federated learning (DFL) to improve the efficiency of distributed machine learning (ML) model training by accounting for communication delays between edge and cloud. Different from traditional federated learning, DFL leverages multiple stochastic gradient descent iterations on local datasets within each global aggregation period and intermittently aggregates model parameters through edge servers in local subnetworks. During global synchronization, the cloud server consolidates local models with the outdated global model using a local-global combiner, thus preserving crucial elements of both, enhancing learning efficiency under the presence of delay. A set of conditions is obtained to achieve the sub-linear convergence rate of O(1/k) for strongly convex and smooth loss functions. Based on these findings, an adaptive control algorithm is developed for DFL, implementing policies to mitigate energy consumption and communication latency while aiming for sublinear convergenc. Numerical evaluations show DFL's superior performance in terms of faster global model convergence, reduced resource consumption, and robustness against communication delays compared to existing FL algorithms. In summary, this proposed method offers improved efficiency and results when dealing with both convex and non-convex loss functions.
Keyword:
Data models
Servers
Training
Delays
Convergence
Adaptation models
Federated learning
edge intelligence
network optimization
convergence analysis
hierarchical architecture
期刊
I
IF:
7
论文数:
1.6K
被引数:
5.5K
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Resource Allocation for Latency-Aware Federated Learning in Industrial Internet of Things面向延迟感知的工业物联网联邦学习资源分配
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