Federated Learning (FL) has emerged as a key approach for distributed machine learning, enhancing online personalization while ensuring user data privacy. Instead of sending private data to a central server as in traditional approaches, FL decentralizes computations: devices train locally and share updates with a global server. A primary challenge in this setting is achieving fast and accurate model training - vital for recommendation systems where delays can compromise user engagement. This paper introduces FedFNN, an algorithm that accelerates decentralized model training. In FL, only a subset of users are involved in each training epoch. FedFNN employs supervised learning to predict weight updates from unsampled users, using updates from the sampled set. Our evaluations, using real and synthetic data, show: 1. FedFNN achieves training speeds 5x faster than leading methods, maintaining or improving accuracy; 2. the algorithm's performance is consistent regardless of client cluster variations; 3. FedFNN outperforms other methods in scenarios with limited client availability, converging more quickly.

翻译：联邦学习（FL）已成为分布式机器学习的关键方法，能够在增强在线个性化服务的同时确保用户数据隐私。与将私有数据发送至中央服务器的传统方法不同，FL将计算过程去中心化：设备在本地进行训练，并与全局服务器共享更新结果。该场景的主要挑战是实现快速且精确的模型训练——这对推荐系统至关重要，因为延迟可能削弱用户参与度。本文提出FedFNN算法，该算法可加速去中心化模型训练。在FL中，每个训练轮次仅涉及部分用户参与。FedFNN采用监督学习，利用采样用户集的更新预测未采样用户的权值更新。基于真实与合成数据的评估表明：1. FedFNN的训练速度相比主流方法提升5倍，同时保持或提升精度；2. 该算法的性能不随客户端集群差异而变化；3. 在客户端可用性受限场景中，FedFNN的收敛速度优于其他方法。