计算机科学
稳健性(进化)
分布式计算
上传
边缘设备
适应性
调度(生产过程)
过程(计算)
GSM演进的增强数据速率
人工智能
机器学习
实时计算
云计算
生态学
生物化学
化学
运营管理
生物
经济
基因
操作系统
作者
Bocheng Chen,Nikolay Ivanov,Guangjing Wang,Qiben Yan
标识
DOI:10.1109/secon58729.2023.10287430
摘要
Federated Learning (FL) is a distributed machine learning (ML) paradigm, aiming to train a global model by exploiting the decentralized data across millions of edge devices. Compared with centralized learning, FL preserves the clients’ privacy by refraining from explicitly downloading their data. However, given the geo-distributed edge devices (e.g., mobile, car, train, or subway) with highly dynamic networks in the wild, aggregating all the model updates from those participating devices will result in inevitable long-tail delays in FL. This will significantly degrade the efficiency of the training process. To resolve the high system heterogeneity in time-sensitive FL scenarios, we propose a novel FL framework, DynamicFL, by considering the communication dynamics and data quality across massive edge devices with a specially designed client manipulation strategy. DynamicFL actively selects clients for model updating based on the network prediction from its dynamic network conditions and the quality of its training data. Additionally, our long-term greedy strategy in client selection tackles the problem of system performance degradation caused by short-term scheduling in a dynamic network. Lastly, to balance the trade-off between client performance evaluation and client manipulation granularity, we dynamically adjust the length of the observation window in the training process to optimize the long-term system efficiency. Compared with the state-of-the-art client selection scheme in FL, DynamicFL can achieve a better model accuracy while consuming only 18.9% – 84.0% of the wallclock time. Our component-wise and sensitivity studies further demonstrate the robustness of DynamicFL under various real-life scenarios.
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