面向自适应预测与模型微调的基于模型混合的个性化联邦学习
Personalized Federated Learning with Mixture of Models for Adaptive Prediction and Model Fine-Tuning
AI总结：
针对动态非平稳环境下预训练模型难以适应流式数据实时预测的问题，本文提出一种结合本地微调模型与服务器多轮联邦模型的个性化联邦学习算法，理论与实验均验证了其在实时预测和联邦微调上的有效性。
摘要：
联邦学习以其在分布式模型训练中的有效性著称，它能让被称为客户端的用户保留数据隐私，无需向协调协作的中央服务器披露自身数据。以往大多数联邦学习研究都假设客户端拥有静态的批量训练数据，但客户端也可能需要在非平稳环境中对流式数据进行实时预测。在这类动态环境中，使用预训练模型可能效率低下，因为它们难以适应不断变化的数据流。为应对这一挑战，客户端可以在线微调模型，利用其观测到的数据来提升性能。尽管客户端参与联邦在线模型微调具备潜在优势，但现有分析尚未确凿证明其优于本地模型微调。为填补这一空白，本文提出了一种新颖的个性化联邦学习算法，其中每个客户端通过将本地微调模型与服务器随时间学习得到的多个联邦模型相结合，构建个性化模型。理论分析与在真实数据集上的实验均证实了该方法在实时预测和联邦模型微调方面的有效性。
英文摘要：
Federated learning is renowned for its efficacy in distributed model training, ensuring that users, called clients, retain data privacy by not disclosing their data to the central server that orchestrates collaborations. Most previous work on federated learning assumes that clients possess static batches of training data. However, clients may also need to make real-time predictions on streaming data in non-stationary environments. In such dynamic environments, employing pre-trained models may be inefficient, as they struggle to adapt to the constantly evolving data streams. To address this challenge, clients can fine-tune models online, leveraging their observed data to enhance performance. Despite the potential benefits of client participation in federated online model fine-tuning, existing analyses have not conclusively demonstrated its superiority over local model fine-tuning. To bridge this gap, the present paper develops a novel personalized federated learning algorithm, wherein each client constructs a personalized model by combining a locally fine-tuned model with multiple federated models learned by the server over time. Theoretical analysis and experiments on real datasets corroborate the effectiveness of this approach for real-time predictions and federated model fine-tuning.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
