While federated learning leverages distributed client resources, it faces challenges due to heterogeneous client capabilities. This necessitates allocating models suited to clients' resources and careful parameter aggregation to accommodate this heterogeneity. We propose HypeMeFed, a novel federated learning framework for supporting client heterogeneity by combining a multi-exit network architecture with hypernetwork-based model weight generation. This approach aligns the feature spaces of heterogeneous model layers and resolves per-layer information disparity during weight aggregation. To practically realize HypeMeFed, we also propose a low-rank factorization approach to minimize computation and memory overhead associated with hypernetworks. Our evaluations on a real-world heterogeneous device testbed indicate that \system enhances accuracy by 5.12% over FedAvg, reduces the hypernetwork memory requirements by 98.22%, and accelerates its operations by 1.86x compared to a naive hypernetwork approach. These results demonstrate HypeMeFed's effectiveness in leveraging and engaging heterogeneous clients for federated learning.

翻译：尽管联邦学习能够利用分布式客户端资源，但由于客户端能力的异构性，其仍面临诸多挑战。这需要分配适合客户端资源的模型，并通过精心的参数聚合来适应这种异构性。我们提出HypeMeFed，一种新颖的联邦学习框架，通过将多出口网络架构与基于超网络的模型权重生成相结合，以支持客户端异构性。该方法对齐了异构模型层的特征空间，并解决了权重聚合过程中各层信息不均衡的问题。为了实际实现HypeMeFed，我们还提出了一种低秩分解方法，以最小化与超网络相关的计算和内存开销。我们在真实世界的异构设备测试平台上进行的评估表明，与FedAvg相比，\system将准确率提高了5.12%；与朴素的超网络方法相比，将超网络内存需求降低了98.22%，并将其运算速度提升了1.86倍。这些结果证明了HypeMeFed在利用和整合异构客户端进行联邦学习方面的有效性。