Recently, Heterogeneous Federated Learning (HtFL) has attracted attention due to its ability to support heterogeneous models and data. To reduce the high communication cost of transmitting model parameters, a major challenge in HtFL, prototype-based HtFL methods are proposed to solely share class representatives, a.k.a, prototypes, among heterogeneous clients while maintaining the privacy of clients' models. However, these prototypes are naively aggregated into global prototypes on the server using weighted averaging, resulting in suboptimal global knowledge which negatively impacts the performance of clients. To overcome this challenge, we introduce a novel HtFL approach called FedTGP, which leverages our Adaptive-margin-enhanced Contrastive Learning (ACL) to learn Trainable Global Prototypes (TGP) on the server. By incorporating ACL, our approach enhances prototype separability while preserving semantic meaning. Extensive experiments with twelve heterogeneous models demonstrate that our FedTGP surpasses state-of-the-art methods by up to 9.08% in accuracy while maintaining the communication and privacy advantages of prototype-based HtFL. Our code is available at https://github.com/TsingZ0/FedTGP.

翻译：近期，异构联邦学习（HtFL）因支持异构模型与数据的能力而备受关注。为降低HtFL中模型参数传输的高通信开销，基于原型的HtFL方法被提出，仅需在异构客户端间共享类别代表（即原型），同时保护客户端模型隐私。然而，这些原型在服务器端通过加权平均被朴素聚合为全局原型，导致全局知识次优，进而对客户端性能产生负面影响。为克服这一挑战，我们提出了一种新型HtFL方法FedTGP，该方法利用自适应余量增强对比学习（ACL）在服务器端学习可训练全局原型（TGP）。通过引入ACL，我们的方法在保持语义意义的同时增强了原型的可分性。在十二种异构模型上的广泛实验表明，FedTGP在保持基于原型的HtFL通信与隐私优势的同时，准确率较现有最先进方法最高提升9.08%。我们的代码开源于https://github.com/TsingZ0/FedTGP。