Title: GoMORE: Global Model Reuse for Resource-Constrained Wireless Federated Learning Show Chinese title

Title: GoMORE：资源受限无线联邦学习中的全局模型复用

Abstract: Due to the dynamics of wireless environment and limited bandwidth, wireless federated learning (FL) is challenged by frequent transmission errors and incomplete aggregation from devices. In order to overcome these challenges, we propose a global model reuse strategy (GoMORE) that reuses the outdated global model to replace the local model parameters once a transmission error occurs. We analytically prove that the proposed GoMORE is strictly superior over the existing strategy, especially at low signal-to-noise ratios (SNRs). In addition, based on the derived expression of weight divergence, we further optimize the number of participating devices in the model aggregation to maximize the FL performance with limited communication resources. Numerical results verify that the proposed GoMORE successfully approaches the performance upper bound by an ideal transmission. It also mitigates the negative impact of non-independent and non-identically distributed (non-IID) data while achieving over 5 dB reduction in energy consumption. Show Chinese abstract

Abstract: 由于无线环境的动态特性和带宽有限，无线联邦学习（FL）面临着频繁的传输错误和设备的不完全聚合的挑战。 为了克服这些挑战，我们提出了一种全局模型重用策略（GoMORE），该策略在发生传输错误时，使用过时的全局模型来替换本地模型参数。 我们分析证明了所提出的GoMORE严格优于现有策略，尤其是在低信噪比（SNRs）情况下。 此外，基于权重发散的推导表达式，我们进一步优化了参与模型聚合的设备数量，以在有限的通信资源下最大化FL性能。 数值结果验证了所提出的GoMORE通过理想传输成功接近性能上限。 它还在实现能量消耗超过5 dB的减少的同时，缓解了非独立同分布（non-IID）数据的负面影响。