Federated Learning (FL) enables collaborative model training among medical
centers without sharing private data. However, traditional FL risks on server
failures and suboptimal performance on local data due to the nature of
centralized model aggregation. To address these issues, we present Gossip
Mutual Learning (GML), a decentralized framework that uses Gossip Protocol for
direct peer-to-peer communication. In addition, GML encourages each site to
optimize its local model through mutual learning to account for data variations
among different sites. For the task of tumor segmentation using 146 cases from
four clinical sites in BraTS 2021 dataset, we demonstrated GML outperformed
local models and achieved similar performance as FedAvg with only 25%
communication overhead.

通过 Gossip Mutual Learning (GML) 这种基于 Gossip 协议的去中心化框架，我们在使用 BraTS 2021 数据集的四个临床站点中，对 146 个病例进行肿瘤分割任务的实验中发现，GML 在与本地模型相比具备显著优势，在只有 25% 通信开销的情况下达到了与 FedAvg 类似的性能。

去中心化 Gossip Mutual Learning（GML）用于多参数 MRI 的脑肿瘤分割

Decentralized Gossip Mutual Learning (GML) for brain tumor segmentation  on multi-parametric MRI

In this paper, we introduce a distributed version of the classical stochastic
Multi-Arm Bandit (MAB) problem. Our setting consists of a large number of
agents $n$ that collaboratively and simultaneously solve the same instance of
$K$ armed MAB to minimize the average cumulative regret over all agents. The
agents can communicate and collaborate among each other \emph{only} through a
pairwise asynchronous gossip based protocol that exchange a limited number of
bits. In our model, agents at each point decide on (i) which arm to play, (ii)
whether to, and if so (iii) what and whom to communicate with. Agents in our
model are decentralized, namely their actions only depend on their observed
history in the past.
We develop a novel algorithm in which agents, whenever they choose,
communicate only arm-ids and not samples, with another agent chosen uniformly
and independently at random. The per-agent regret scaling achieved by our
algorithm is $O \left( \frac{\lceil\frac{K}{n}\rceil+\log(n)}{\Delta}
\log(T) + \frac{\log^3(n) \log \log(n)}{\Delta^2}
\right)$. Furthermore, any agent in our algorithm communicates only a total
of $\Theta(\log(T))$ times over a time interval of $T$.
We compare our results to two benchmarks - one where there is no
communication among agents and one corresponding to complete interaction. We
show both theoretically and empirically, that our algorithm experiences a
significant reduction both in per-agent regret when compared to the case when
agents do not collaborate and in communication complexity when compared to the
full interaction setting which requires $T$ communication attempts by an agent
over $T$ arm pulls.

介绍了一个分布式算法来解决多臂赌博机问题，通过异步交换较少的比特数，在不进行样本交换的情况下，仅通过传递臂 ID 来维护代理之间的合作；文中提出的算法可以将每个代理的后悔最小化，并将通信复杂度降至 $O (logT)$，与不进行合作的方案相比，本算法能够显著降低每个代理的后悔。