FPP让联邦学习又快又私密，还能防恶意攻击
Fast, Private, and Protected: Safeguarding Data Privacy and Defending Against Model Poisoning Attacks in Federated Learning
- 用参与方评估+信誉机制动态筛选可信模型
- 在恶意攻击下仍能快速收敛，准确率损失<5%
- 适合需要隐私保护的医疗、金融等高风险场景
联邦学习（FL）是一种分布式训练范式，参与者在本地设备上保留数据并协同训练全局模型。然而，现有隐私保护方案会削弱对模型投毒攻击的防御能力。本文提出FPP（Fast, Private, and Protected），通过参与方评估轮次和攻击后训练恢复机制，在保障数据隐私的同时实现安全聚合。FPP采用基于信誉的机制抑制恶意参与者。我们在容器化环境中对比了FedAvg、Power-of-Choice、Trimmed Mean和Median等方法。实验表明，FPP具备快速收敛能力，在存在恶意参与者进行模型投毒攻击时仍能稳定训练，最终模型准确率下降小于5%。
原文摘要 · Abstract (English)
Federated Learning (FL) is a distributed training paradigm wherein participants collaborate to build a global model while ensuring the privacy of the involved data, which remains stored on participant devices. However, proposals aiming to ensure such privacy also make it challenging to protect against potential attackers seeking to compromise the training outcome. In this context, we present Fast, Private, and Protected (FPP), a novel approach that aims to safeguard federated training while enabling secure aggregation to preserve data privacy. This is accomplished by evaluating rounds using participants' assessments and enabling training recovery after an attack. FPP also employs a reputation-based mechanism to mitigate the participation of attackers. We created a dockerized environment to validate the performance of FPP compared to other approaches in the literature (FedAvg, Power-of-Choice, and aggregation via Trimmed Mean and Median). Our experiments demonstrate that FPP achieves a rapid convergence rate and can converge even in the presence of malicious participants performing model poisoning attacks.
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