期刊
IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS
卷 19, 期 11, 页码 7690-7703出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TWC.2020.3015671
关键词
Scheduling; Convergence; Servers; Wireless communication; Processor scheduling; Probabilistic logic; Resource management; Federated edge learning; scheduling; multiuser diversity; resource management; convergence analysis
资金
- National Science Foundation [CCF-1910168, CNS-2003098]
- Hong Kong Research Grants Council [17208319, 17209917]
- Innovation and Technology Fund [GHP/016/18GD]
- Guang-dong Basic and Applied Basic Research Foundation [2019B1515130003]
In cellular federated edge learning (FEEL), multiple edge devices holding local data jointly train a neural network by communicating learning updates with an access point without exchanging their data samples. With very limited communication resources, it is beneficial to schedule the most informative local learning updates. This paper focuses on FEEL with gradient averaging over participating devices in each round of communication. A novel scheduling policy is proposed to exploit both diversity in multiuser channels and diversity in the importance of the edge devices' learning updates. First, a new probabilistic scheduling framework is developed to yield unbiased update aggregation in FEEL. The importance of a local learning update is measured by its gradient divergence. If one edge device is scheduled in each communication round, the scheduling policy is derived in closed form to achieve the optimal trade-off between channel quality and update importance. The probabilistic scheduling framework is then extended to allow scheduling multiple edge devices in each communication round. Numerical results obtained using popular models and learning datasets demonstrate that the proposed scheduling policy can achieve faster model convergence and higher learning accuracy than conventional scheduling policies that only exploit a single type of diversity.
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