CovFedAvg 提出联邦模型选择新方法，Gas 任务宏平衡准确率提升至 89.84%
CovFedAvg: Local Low-Loss Curves and Federated Model Selection
一篇联邦学习论文，思路是让各客户端不用交换数据也能收敛到行为兼容的共享模型，在 gas 传感器任务上比 FedAvg 高了约 1 个点。
arXiv 论文提出 CovFedAvg，让客户端保留本地模型与曲线控制，通过聚合梯度贡献拟合共同端点，再用分布式训练损失完成模型选择，全程不需要中心化校准样本或客户端间样本交换。该方法保证选出的模型评估训练损失不高于上一轮模型和当轮 FedAvg 候选，并给出条件性客户端兼容与近似选择的理论界。实验覆盖合成支持任务、活动识别受试者数据和 gas 传感器批次，共测试五种方法、五个种子、两种网络宽度和 20 轮通信。在宽度 16 的参与方 Gas 任务上，CovFedAvg 将宏平衡准确率从 FedAvg 的 88.88% 提升到 89.84%，匹配成本的分布式微调则达到 91.65%。
CovFedAvg: Local Low-Loss Curves and Federated Model Selection
Clients observing different parts of a prediction task may learn locally equivalent models with incompatible behavior outside their observed support. We study whether redundant models permit local low-loss paths toward a compatible shared model. CovFedAvg retains local models and curve controls on clients, fits a common endpoint through aggregated gradient contributions, and selects an actual candidate model using distributed training losses. No centralized calibration examples or client-to-client sample exchange are required. Exact selection guarantees no higher evaluated training loss than the previous model and the current-round FedAvg candidate; this is not a global-optimality, stationarity, generalization, or differential-privacy guarantee. We state conditional client-compatibility and approximate-selection bounds and retain corrected transformation identities and a separate full-gradient safeguard theorem for reference algorithms. A complementary input-support example recovers the shared absolute-value regression, while matched direct fine-tuning also solves it. Across a synthetic support task, recorded activity-recognition subjects, and participating and future gas-sensor batches, we evaluate five methods, five seeds, two network widths, and 20 rounds without calibration data. At width 16, CovFedAvg raises participating-Gas macro balanced accuracy from FedAvg's 88.88% to 89.84%; matched distributed fine-tuning reaches 91.65%. Results support feasibility and modest gains over FedAvg, but do not establish a curve-specific advantage over equally costly optimization. Exact-sum numerical experiments establish data flow; end-to-end privacy requires additional mechanisms and analysis.