A novel Decentralized Noisy Model Update Tracking Federated Learning algorithm (FedNMUT) is proposed, which is tailored to function efficiently in the presence of noisy communication channels that reflect imperfect information exchange. This algorithm uses gradient tracking to minimize the impact of data heterogeneity while minimizing communication overhead. The proposed algorithm incorporates noise into its parameters to mimic the conditions of noisy communication channels, thereby enabling consensus among clients through a communication graph topology in such challenging environments. FedNMUT prioritizes parameter sharing and noise incorporation to increase the resilience of decentralized learning systems against noisy communications. Through theoretical and empirical validation, it is demonstrated that the performance of FedNMUT is superior compared to the existing state-of-the-art methods and conventional parameter-mixing approaches in dealing with imperfect information sharing. This proves the capability of the proposed algorithm to counteract the negative effects of communication noise in a decentralized learning framework.

翻译：提出了一种新型去中心化噪声模型更新追踪联邦学习算法（FedNMUT），该算法专为存在反映非完美信息交换的噪声通信信道的高效运行而设计。该算法利用梯度追踪最小化数据异质性的影响，同时降低通信开销。通过在参数中引入噪声来模拟噪声通信信道的条件，算法能够在如此具有挑战性的环境中通过通信图拓扑实现客户端间的共识。FedNMUT优先考虑参数共享和噪声引入，以增强去中心化学习系统对抗噪声通信的鲁棒性。通过理论与实证验证，证明FedNMUT在处理非完美信息共享方面的性能优于现有最先进方法与传统的参数混合方法。这证明了所提算法在去中心化学习框架中抵消通信噪声负面效应的能力。