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.

提出了一种新颖的分散噪声模型更新跟踪联邦学习算法 (FedNMUT)，该算法在存在反映不完善信息交换的噪声通信通道的情况下高效运行。该算法使用梯度跟踪来最小化数据异质性的影响，同时也最小化通信开销。该算法将噪声纳入其参数中，以模拟噪声通信通道的条件，在这样具有挑战性的环境中通过通信图拓扑实现客户端之间的一致性。FedNMUT 优先考虑参数共享和噪声纳入，以增加去中心化学习系统对噪声通信的抵抗能力。通过理论和实证验证，证明了与现有最先进方法和传统参数混合方法相比，FedNMUT 的性能更好，在处理不完善信息共享方面具有优势。这证明了所提算法在去中心化学习框架中抵消通信噪声的负面影响的能力。

分散联邦学习：在信息共享不完善情况下的模型更新跟踪

Decentralized Federated Learning: Model Update Tracking Under Imperfect  Information Sharing

The rapid proliferation of decentralized learning systems mandates the need
for differentially-private cooperative learning. In this paper, we study this
in context of the contextual linear bandit: we consider a collection of agents
cooperating to solve a common contextual bandit, while ensuring that their
communication remains private. For this problem, we devise \textsc{FedUCB}, a
multiagent private algorithm for both centralized and decentralized
(peer-to-peer) federated learning. We provide a rigorous technical analysis of
its utility in terms of regret, improving several results in cooperative bandit
learning, and provide rigorous privacy guarantees as well. Our algorithms
provide competitive performance both in terms of pseudoregret bounds and
empirical benchmark performance in various multi-agent settings.

本文就基于上下文线性赌博机的联邦学习问题提出了一种称为 FedUCB 的多代理私有算法，该算法在中央化和去中心化（点对点）联邦学习方案中均可使用，在保证通信隐私的同时，在后遗憾度和隐私保证方面表现出极强的实用性。