提出域感知代理数据选择，提升联邦蒸馏在分布外场景下的性能
Out-of-Distribution Federated Distillation with Domain-Aware Proxy
- 基于域感知机制筛选更适配的代理数据
- 在无/有代理数据下分别达82.9%和80.6%准确率
- 适合关注联邦学习泛化能力的研究者
联邦学习是一种分布式机器学习范式，通过聚合本地客户端模型而不共享私有数据来训练全局模型。联邦蒸馏（FD）在此基础上引入知识蒸馏，以代理数据上的软预测代替模型参数交换，实现更高效的通信并支持异构模型协作。然而，仅在分布内数据上训练的FD模型难以适应分布外（OOD）场景。本文提出一种域感知代理选择框架，以更好应对分布外问题。实验表明，所提模型在标准基准上，无论有无代理数据，均分别达到82.9%和80.6%的平均准确率，优于现有方法。代码与数据已公开于https://anonymous.4open.science/r/DPS-FD-8596。
原文摘要 · Abstract (English)
Federated Learning is a distributed machine learning paradigm that trains a global model by aggregating local clients without sharing private data of each client. Federated Distillation (FD) builds upon this paradigm by leveraging knowledge distillation to exchange soft predictions on proxy data instead of model parameters, enabling more efficient communication and supporting heterogeneous model collaboration. However, FD models trained on In-Distribution data are hardly adapted to Out-of-Distribution (OOD) scenarios. In this paper, we propose a domain-aware proxy selection framework to better adopt proxy data for OOD problems. The experimental results show that the proposed models effectively address the challenges of distribution shifts under OOD with and without proxy data by achieving average 82.9\% and 80.6\% over existing works on standard benchmarks. The codes and data are released in https://anonymous.4open.science/r/DPS-FD-8596.
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