用民主投票机制选最优客户端，省传输还抗攻击
Fluid Democracy in Federated Data Aggregation
- 用共识协议选出最有价值的客户端参与聚合
- 新算法在相同条件下性能优于传统方法，且防权力累积
- 动态抑制恶意客户端影响，适合高对抗场景
联邦学习通常要求所有客户端向中心服务器上传模型权重，无论其有效性如何。为避免不必要的数据传输开销，本文提出基于共识的协议，在每轮通信中识别出最具价值的客户端子集。首先，从性能角度评估现有流体民主协议在联邦学习中的应用，并与传统的一人一票（1p1v，即FedAvg）方法进行对比。本文提出一种名为黏滞保留民主（viscous-retained democracy, FedVRD）的新协议，在相同假设下始终优于1p1v，且不会导致影响力累积。其次，从对抗视角揭示现有流体民主协议在拓扑依赖性和所需攻击者数量方面的弱点。为此，我们设计了FedVRD算法，通过利用委托拓扑动态限制恶意客户端的影响，同时最小化通信成本。
原文摘要 · Abstract (English)
Federated learning (FL) mechanisms typically require each client to transfer their weights to a central server, irrespective of how useful they are. In order to avoid wasteful data transfer costs from clients to the central server, we propose the use of consensus based protocols to identify a subset of clients with most useful model weights at each data transfer step. First, we explore the application of existing fluid democracy protocols to FL from a performance standpoint, comparing them with traditional one-person-one-vote (also known as 1p1v or FedAvg). We propose a new fluid democracy protocol named viscous-retained democracy that always does better than 1p1v under the same assumptions as existing fluid democracy protocols while also not allowing for influence accumulation. Secondly, we identify weaknesses of fluid democracy protocols from an adversarial lens in terms of their dependence on topology and/ or number of adversaries required to negatively impact the global model weights. To this effect, we propose an algorithm (FedVRD) that dynamically limits the effect of adversaries while minimizing cost by leveraging the delegation topology.
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