Asynchronous Federated Learning with Bidirectional Quantized Communications and Buffered Aggregation
Asynchronous Federated Learning with Bidirectional Quantized Communications and Buffered Aggregation
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DOI：
10.48550/arxiv.2308.00263
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发表时间：
2023-08
期刊：
影响因子：
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通讯作者：
Tomàs Ortega;H. Jafarkhani
中科院分区：
文献类型：
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作者：
Tomàs Ortega;H. Jafarkhani
带缓冲聚合的异步联合学习（FedBuff）是一种最先进的算法，以其效率和高可扩展性而闻名。然而，它有一个高的通信成本，这还没有与量化的通信检查。为了解决这个问题，我们提出了一种新的算法（QAFeL），与量化方案，建立一个共享的“隐藏”状态之间的服务器和客户端，以避免直接量化所造成的错误传播。这种方法允许高精度，同时显著减少客户端-服务器交互期间传输的数据。我们提供理论上的收敛保证QAFEL和证实我们的分析与实验上的标准基准。
Asynchronous Federated Learning with Buffered Aggregation (FedBuff) is a state-of-the-art algorithm known for its efficiency and high scalability. However, it has a high communication cost, which has not been examined with quantized communications. To tackle this problem, we present a new algorithm (QAFeL), with a quantization scheme that establishes a shared"hidden"state between the server and clients to avoid the error propagation caused by direct quantization. This approach allows for high precision while significantly reducing the data transmitted during client-server interactions. We provide theoretical convergence guarantees for QAFeL and corroborate our analysis with experiments on a standard benchmark.