去中心化联邦学习让多方协作训练生成分类器，无需中央服务器。
Decentralized Federated Learning of Probabilistic Generative Classifiers
- 各设备直接通信共享局部统计信息，迭代更新本地模型。
- 在多种网络结构和非独立同分布数据下均能收敛到高性能全局模型。
- 适合隐私敏感场景，尤其适用于无中心节点的分布式系统。
联邦学习是一种日益重要的范式，旨在不共享私有数据的前提下，通过异构用户网络构建全局模型。本文聚焦于去中心化架构下的模型学习，用户之间直接协作更新全局模型，无需依赖中心服务器。提出一种新型方法，用于协同学习具有参数形式的概率生成分类器。该框架包含一个由多个本地节点构成的通信网络，每个节点拥有本地数据及本地更新规则。通过与邻近节点共享局部统计信息，每个节点聚合邻居信息并迭代优化自身本地分类器，逐步逼近全局模型。大量实验表明，该算法在多种网络拓扑、网络规模、本地数据集大小以及极端非独立同分布数据分布下，均能稳定收敛至性能优异的全局模型。
原文摘要 · Abstract (English)
Federated learning is a paradigm of increasing relevance in real world applications, aimed at building a global model across a network of heterogeneous users without requiring the sharing of private data. We focus on model learning over decentralized architectures, where users collaborate directly to update the global model without relying on a central server. In this context, the current paper proposes a novel approach to collaboratively learn probabilistic generative classifiers with a parametric form. The framework is composed by a communication network over a set of local nodes, each of one having its own local data, and a local updating rule. The proposal involves sharing local statistics with neighboring nodes, where each node aggregates the neighbors' information and iteratively learns its own local classifier, which progressively converges to a global model. Extensive experiments demonstrate that the algorithm consistently converges to a globally competitive model across a wide range of network topologies, network sizes, local dataset sizes, and extreme non-i.i.d. data distributions.
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