Federated learning (FL) enables multiple participants to train a global
machine learning model without sharing their private training data.
Peer-to-peer (P2P) FL advances existing centralized FL paradigms by eliminating
the server that aggregates local models from participants and then updates the
global model. However, P2P FL is vulnerable to (i) honest-but-curious
participants whose objective is to infer private training data of other
participants, and (ii) Byzantine participants who can transmit arbitrarily
manipulated local models to corrupt the learning process. P2P FL schemes that
simultaneously guarantee Byzantine resilience and preserve privacy have been
less studied. In this paper, we develop Brave, a protocol that ensures
Byzantine Resilience And privacy-preserving property for P2P FL in the presence
of both types of adversaries. We show that Brave preserves privacy by
establishing that any honest-but-curious adversary cannot infer other
participants' private data by observing their models. We further prove that
Brave is Byzantine-resilient, which guarantees that all benign participants
converge to an identical model that deviates from a global model trained
without Byzantine adversaries by a bounded distance. We evaluate Brave against
three state-of-the-art adversaries on a P2P FL for image classification tasks
on benchmark datasets CIFAR10 and MNIST. Our results show that the global model
learned with Brave in the presence of adversaries achieves comparable
classification accuracy to a global model trained in the absence of any
adversary.

通过消除既有集中式联邦学习范式中汇总本地模型的服务器，将被称为 P2P FL 的方法应运而生，然而，P2P FL 容易受到善意但好奇的参与者和可以发出任意篡改的拜占庭参与者的攻击，本文提出了一种名为 Brave 的协议，以确保 P2P FL 在这两种类型的对手存在下同时保证了拜占庭容错和隐私保留的性质，并通过对 CIFAR10 和 MNIST 数据集上的图像分类任务进行评估，结果表明 Brave 可在对手存在的情况下实现与无对手存在时相当的分类准确性。

Brave：拜占庭容错和隐私保护的点对点联邦学习

Brave: Byzantine-Resilient and Privacy-Preserving Peer-to-Peer Federated  Learning

In federated learning (FL), clients may have diverse objectives, and merging
all clients' knowledge into one global model will cause negative transfer to
local performance. Thus, clustered FL is proposed to group similar clients into
clusters and maintain several global models. In the literature, centralized
clustered FL algorithms require the assumption of the number of clusters and
hence are not effective enough to explore the latent relationships among
clients. In this paper, without assuming the number of clusters, we propose a
peer-to-peer (P2P) FL algorithm named PANM. In PANM, clients communicate with
peers to adaptively form an effective clustered topology. Specifically, we
present two novel metrics for measuring client similarity and a two-stage
neighbor matching algorithm based Monte Carlo method and Expectation
Maximization under the Gaussian Mixture Model assumption. We have conducted
theoretical analyses of PANM on the probability of neighbor estimation and the
error gap to the clustered optimum. We have also implemented extensive
experiments under both synthetic and real-world clustered heterogeneity.
Theoretical analysis and empirical experiments show that the proposed algorithm
is superior to the P2P FL counterparts, and it achieves better performance than
the centralized cluster FL method. PANM is effective even under extremely low
communication budgets.

本文提出了一种名为 PANM 的分布式学习算法，该算法适用于异构且分散的客户端，并通过对等通信自适应地形成一个有效的聚类拓扑结构。经过理论分析和大量实验证明，该算法的性能优于传统的集中式聚类 FL 方法和其他 P2P 方法。