Abstract
Federated learning is a distributed machine learning framework which enables different parties to collaboratively train a model while protecting data privacy and security. This form of privacy-preserving collaborative learning comes at the cost of a significant communication overhead during training. Another key challenge in federated learning is to handle the heterogeneity of local data distribution across parties. To solve these problems, we proposed a novel two-stream communication-efficient federated pruning network (FedPrune), which consists of two parts: in the downstream stage, deep reinforcement learning is used to adaptively prune each layer of global model to reduce downstream communication costs; in the upstream stage, a pruning method based on the proximal operator is proposed to reduce the upstream communication costs as well as limit the drift of the local update, which is robust to non-IID client data. FedPrune is tested on three DNN models and publicly available datasets. The results demonstrate that it can well control the training overhead while still guaranteeing the learning performance.
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Acknowledgments
This work was supported in part by the National Natural Science Foundation of China under Grant 62076179, in part by the Beijing Natural Science Foundation under Grant Z180006.
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Gu, S., Yang, L., Deng, S., Xu, Z. (2022). Two-Stream Communication-Efficient Federated Pruning Network. In: Khanna, S., Cao, J., Bai, Q., Xu, G. (eds) PRICAI 2022: Trends in Artificial Intelligence. PRICAI 2022. Lecture Notes in Computer Science, vol 13631. Springer, Cham. https://doi.org/10.1007/978-3-031-20868-3_14
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