Auto-FL-Research：联邦学习算法的代理搜索

arXiv:2607.01366v1 Announce Type: new Abstract: Federated learning (FL) research often depends on many small but consequential algorithmic choices: optimizer variants, server aggregation rules, local training schedules, normalization, regularization, and model architecture.手动探索这些选择的成本很高，而且当候选者的变化也会改变 FL 训练或评估路径时，很难公平地进行比较。在这项工作中，我们提出了 Auto-FL-Research (AFR)，这是一种用于 FL 算法配方搜索的受限编码代理工作流程。代理可以提出并实现候选训练算法，包括服务器聚合规则、客户端更新计划、本地目标和注册的模型变体，而任务配置文件则修复突变表面、计算预算、通信契约和最终模型评估。每个活动都会记录候选人分数、运行时间、编辑的文件、工件和失败状态。 We evaluate AFR on five healthcare cross-silo FLamby tasks and on grouped-client profiles for the five fixed LEAF datasets plus the LEAF synthetic task. Five-seed repeat evaluations support gains on four FLamby tasks and five of six LEAF profiles, while also exposing seed-sensitive and search-selected failure cases. Same-budget controls show that several gains correspond to FL-recipe changes, whereas other improvements are recovered by fixed-surface scalar controls or fail under repeat or held-out evaluation. These mixed outcomes are part of the contribution: they show how agent-generated candidates can be separated into repeated FL mechanisms, fixed-surface tuning effects, and selected single-run artifacts.