FedRE：面向模型异构联邦学习的表征纠缠框架
FedRE: A Representation Entanglement Framework for Model-Heterogeneous Federated Learning
对视觉研究者而言，这是一种在客户端模型架构不一致时仍能聚合表征的联邦训练范式：用归一化随机权重把本地特征与 one-hot 标签编码分别聚合成单一纠缠表征与纠缠标签编码，再上传服务器训练全局分类器，可作为异构端侧视觉模型协同训练与隐私友好表征共享的可复用方案。
原文依据 each client aggregates its local representations into a single entangled representation using normalized random weights
摘要译文
由模型翻译 · 术语统一 · 请对照原文核对关键结论联邦学习（FL）能够在保护隐私的前提下实现跨客户端的协同训练。现有大多数联邦学习方法假设各客户端模型架构同构，但客户端在数据与资源上的异构性使这一假设难以成立，从而催生了模型异构联邦学习。为解决该问题，我们提出联邦表征纠缠（FedRE），一个建立在一种称为纠缠表征的新型客户端知识之上的框架。具体而言，每个客户端使用归一化随机权重将本地表征聚合为单一纠缠表征，随后用相同权重将对应的一热标签编码整合为纠缠标签编码。二者随后被上传至服务器以训练全局分类器。在训练过程中，每个纠缠表征通过其纠缠标签编码获得跨类别的监督，而随机权重在每一轮重新采样以引入多样性，从而缓解全局分类器的过度自信并得到更平滑的决策边界。此外，每个客户端仅上传单一纠缠表征及其纠缠标签编码，降低了表征反演攻击的风险并减少了通信开销。大量实验表明，FedRE 在模型性能、隐私保护与通信开销之间取得了有效折中。代码见 https://github.com/AIResearch-Group/FedRE。
英文原文摘要 1494 字符
Federated learning (FL) enables collaborative training across clients while preserving privacy. While most existing FL methods assume homogeneous model architectures, client heterogeneity in both data and resources makes this assumption impractical, thus motivating model-heterogeneous FL. To address this problem, we propose Federated Representation Entanglement (FedRE), a framework built upon a novel form of client knowledge termed entangled representation. Specifically, each client aggregates its local representations into a single entangled representation using normalized random weights, and then applies the same weights to integrate the corresponding one-hot label encodings into an entangled-label encoding. Both are subsequently uploaded to the server to train a global classifier. During training, each entangled representation is supervised across categories via its entangled-label encoding, while random weights are re-sampled at each round to introduce diversity, alleviating overconfidence in the global classifier and yielding smoother decision boundaries. Moreover, each client uploads a single entangled representation along with its entangled-label encoding, mitigating the risk of representation inversion attacks and reducing communication overhead. Extensive experiments demonstrate that FedRE achieves an effective trade-off among model performance, privacy protection, and communication overhead. The codes are available at https://github.com/AIResearch-Group/FedRE.