FedJigsaw：面向去中心化异构联邦学习的多智能体协作模型重组
FedJigsaw: Multi-Agent Collaborative Model Reassembly for Decentralized Heterogeneous Federated Learning
AI总结：
FedJigsaw是面向去中心化异构联邦学习的多智能体协作模型重组框架，通过AttenAssemble、SymbioArchitect、CoRe-Tune机制实现个性化模型构建，性能优于现有基线且延迟与内存开销更低。
AI中文摘要：
模型异构联邦学习（MHFL）通过允许各参与方在共享训练目标下训练个性化模型架构，解决客户端层面的资源异构问题。现有主流的部分训练（PT）范式允许每个客户端训练全局模型的一个子网络，但这类方法通常依赖预定义的架构模板或过参数化的超网络，限制了细粒度个性化，且带来大量计算和内存开销。本文提出FedJigsaw，一种将模型个性化重塑为动态去中心化模型组装问题的新型框架：客户端不从预定义超网络中选择子网络，而是通过组装从邻近客户端学到的可复用模块构建自身模型。在客户端层面，本文引入AttenAssemble机制，使各参与方能基于本地观测自适应构建定制化模型；为在通信和隐私约束下支持高效知识共享，设计SymbioArchitect机制，允许客户端与其拓扑邻居交换细粒度模型模块；为缓解去中心化模块交换带来的训练不稳定性，设计CoRe-Tune，一种带注意力增强的集中式训练与去中心化执行策略，在不损害数据隐私的前提下引导本地策略促进隐式协作、稳定训练动态。大量评估表明，FedJigsaw在相对准确率上比现有最优MHFL基线高出最多13.8%，同时大幅降低客户端间性能方差，相比现有策略驱动方法还显著缩短决策延迟、削减峰值内存占用。
英文摘要：
Model Heterogeneous Federated Learning (MHFL) addresses client-level resource heterogeneity by allowing each participant to train a personalized model architecture under a shared training objective. A prevalent paradigm, Partial Training (PT), achieves this by allowing each client to train a subnetwork of the global model. However, existing PT methods typically rely on predefined architectural templates or over-parameterized supernets, limiting fine-grained personalization and imposing substantial computational and memory overhead. We propose FedJigsaw, a novel framework that reshapes model personalization as a dynamic and decentralized model assembly problem. Instead of selecting subnetworks from a predefined supernetwork, each client constructs its model by assembling reusable modules learned from neighboring clients. At the client level, we introduce AttenAssemble to enable each participant to adaptively construct a tailored model based on local observations. To support efficient knowledge sharing under communication and privacy constraints, we design SymbioArchitect, a mechanism that allows clients to exchange granular model modules with their topological neighbors. To mitigate training instability introduced by decentralized module exchange, we design CoRe-Tune, an attention-enhanced centralized training with a decentralized execution strategy, which guides local policies to foster implicit collaboration and stabilize training dynamics, without compromising data privacy. Extensive evaluations demonstrate that FedJigsaw outperforms state-of-the-art MHFL baselines by up to 13.8% in relative accuracy while significantly shrinking cross-client performance variance, but also slashes decision-making latency and peak memory footprint compared to existing policy-driven methods.
