Federated learning enables users to collaboratively train a machine learning model over their private datasets. Secure aggregation protocols are employed to mitigate information leakage about the local datasets. This setup, however, still leaks the participation of a user in a training iteration, which can also be sensitive. Protecting user anonymity is even more challenging in dynamic environments where users may (re)join or leave the training process at any point of time. In this paper, we introduce AnoFel, the first framework to support private and anonymous dynamic participation in federated learning. AnoFel leverages several cryptographic primitives, the concept of anonymity sets, differential privacy, and a public bulletin board to support anonymous user registration, as well as unlinkable and confidential model updates submission. Additionally, our system allows dynamic participation, where users can join or leave at any time, without needing any recovery protocol or interaction. To assess security, we formalize a notion for privacy and anonymity in federated learning, and formally prove that AnoFel satisfies this notion. To the best of our knowledge, our system is the first solution with provable anonymity guarantees. To assess efficiency, we provide a concrete implementation of AnoFel, and conduct experiments showing its ability to support learning applications scaling to a large number of clients. For an MNIST classification task with 512 clients, the client setup takes less than 3 sec, and a training iteration can be finished in 3.2 sec. We also compare our system with prior work and demonstrate its practicality for contemporary learning tasks.

翻译：联邦学习使用户能够在其私有数据集上协作训练机器学习模型。采用安全聚合协议以减轻本地数据集的信息泄露风险。然而该设置仍会泄露用户在某次训练迭代中的参与行为，而这种参与信息本身可能具有敏感性。在用户可随时（重新）加入或离开训练过程的动态环境中，保护用户匿名性更具挑战性。本文提出AnoFel，这是首个支持联邦学习中匿名动态参与的框架。AnoFel利用多种密码学原语、匿名集概念、差分隐私以及公共公告板，支持匿名用户注册、不可关联且保密的模型更新提交。此外，系统支持动态参与——用户可随时加入或退出，无需任何恢复协议或交互。在安全性评估方面，我们形式化定义了联邦学习中的隐私与匿名性概念，并严格证明AnoFel满足该定义。据我们所知，本系统是首个具有可证明匿名性保证的解决方案。在效率评估方面，我们实现了AnoFel的具体原型，并通过实验证明其能支撑大规模客户端的学习任务。对于包含512个客户端的MNIST分类任务，客户端初始设置耗时不足3秒，单次训练迭代可在3.2秒内完成。我们还与现有工作进行对比，证明了系统在当代学习任务中的实用性。