Secure model aggregation is a key component of federated learning (FL) that aims at protecting the privacy of each user's individual model while allowing for their global aggregation. It can be applied to any aggregation-based FL approach for training a global or personalized model. Model aggregation needs to also be resilient against likely user dropouts in FL systems, making its design substantially more complex. State-of-the-art secure aggregation protocols rely on secret sharing of the random-seeds used for mask generations at the users to enable the reconstruction and cancellation of those belonging to the dropped users. The complexity of such approaches, however, grows substantially with the number of dropped users. We propose a new approach, named LightSecAgg, to overcome this bottleneck by changing the design from "random-seed reconstruction of the dropped users" to "one-shot aggregate-mask reconstruction of the active users via mask encoding/decoding". We show that LightSecAgg achieves the same privacy and dropout-resiliency guarantees as the state-of-the-art protocols while significantly reducing the overhead for resiliency against dropped users. We also demonstrate that, unlike existing schemes, LightSecAgg can be applied to secure aggregation in the asynchronous FL setting. Furthermore, we provide a modular system design and optimized on-device parallelization for scalable implementation, by enabling computational overlapping between model training and on-device encoding, as well as improving the speed of concurrent receiving and sending of chunked masks. We evaluate LightSecAgg via extensive experiments for training diverse models on various datasets in a realistic FL system with large number of users and demonstrate that LightSecAgg significantly reduces the total training time.

翻译：安全模型聚合是联合代金学习的一个关键组成部分,目的是保护每个用户个人模型的隐私,同时允许其全球汇总。它可以应用于基于汇总的FL方法,用于培训一个全球或个性化模型。模型聚合还需要适应FL系统中可能用户辍学的情况,使其设计更为复杂。最先进的安全集合协议依赖于秘密分享用户代代代代代代代代代代代代代代代代代代代代代代代代代代代代代代代代代代代代代代代代代代代代代代代代代代代代代代代代代代代代代代代代代代代代代代代代代代代代代代代代代代代代代代代代代代代代代代代代代代代代代代代代代代代代代代代代代代代代代代代代代代代代代代代代代代代代代代代代代代代代代代代代代代代代代代代代代代代代代代代代代代代代代代代代代代代代代代代代代代代代代代代代代代代代代代代代代代代代代代代代代代代代代代代代代代代代代代代代代代代代代代代代代代代代代代代代代代代代代代代代代代代代代代代代代代代代代代代代代代代代代代代代代代代代代代代代代代代代代代代代代代代代代代代代代代代代代代代代代代代代代代代代代代代代代代代代代代代代代代代代代代代代代代代代代代代代代代代代代代代代代代代代代代代代代代代代代代代代代代代代代代代代代代代代代代代代代代代代代代代代代代代代代代代代代代代代代代代代代代代代代代代代代代代代代代代代代代代代代代代代代代代代代代代代代代代代代代代代代代