标题： 快速、私有和受保护：在联邦学习中保护数据隐私并防御模型污染攻击 显示英文标题

标题： Fast, Private, and Protected: Safeguarding Data Privacy and Defending Against Model Poisoning Attacks in Federated Learning

摘要： 联邦学习（FL）是一种分布式训练范式，其中参与者合作构建一个全局模型，同时确保所涉及数据的隐私，这些数据保留在参与者设备上。 然而，旨在确保这种隐私的提议也使得抵御试图破坏训练结果的潜在攻击者变得具有挑战性。 在这一背景下，我们提出了快速、私有和受保护（FPP）的方法，这是一种新的方法，旨在保护联邦训练的同时，实现安全聚合以保护数据隐私。 这是通过使用参与者的评估来评估轮次，并在攻击后启用训练恢复来实现的。 FPP还采用基于声誉的机制来减轻攻击者的参与。 我们创建了一个容器化环境，以验证FPP与其他文献中的方法（FedAvg、Power-of-Choice以及通过截断均值和中位数进行聚合）的性能。 我们的实验表明，FPP具有快速的收敛速度，并且即使在恶意参与者执行模型中毒攻击的情况下也能收敛。 显示英文摘要

摘要： 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.