Federated Learning with Server Learning: Enhancing Performance for Non-IID Data

Federated Learning (FL) has emerged as a means of distributed learning using local data stored at clients with a coordinating server. Recent studies showed that FL can suffer from poor performance and slower convergence when training data at clients are not independent and identically distributed. Here we consider a new complementary approach to mitigating this performance degradation by allowing the server to perform auxiliary learning from a small dataset. Our analysis and experiments show that this new approach can achieve significant improvements in both model accuracy and convergence time even when the server dataset is small and its distribution differs from that of the aggregated data from all clients.Comment: 22 pages, 11 figures, 3 table

3,3-Dinitro­azetidinium 2-hy­droxy­benzoate

In the title gem-dinitro­azetidinium 2-hy­droxy­benzoate salt, C3H6N3O4 +·C7H5O3 −, the azetidine ring is virtually planar, with a mean deviation from the plane of 0.0242 Å. The dihedral angle between the two nitro groups is 87.5 (1)°

Functional Outcomes Among Patients With Acute Ischemic Stroke After Mechanical Thrombectomy With or Without Intravenous Thrombolysis

To the Editor In their randomized clinical trial, Dr Suzuki and colleagues1 reported that mechanical thrombectomy alone failed to demonstrate noninferiority compared with combined intravenous thrombolysis and mechanical thrombectomy with regard to favorable functional outcome at 90 days in patients with acute ischemic stroke. We have some concerns about this study