3 papers
cs.LG2025
Sketched Gaussian Mechanism for Private Federated Learning
Qiaobo Li, Zhijie Chen, Arindam Banerjee
Communication cost and privacy are two major considerations in federated learning (FL). For communication cost, gradient compression by sketching the clients' transmitted model upd…
cs.LG2025
Sketched Adaptive Federated Deep Learning: A Sharp Convergence Analysis
Zhijie Chen, Qiaobo Li, Arindam Banerjee
Combining gradient compression methods (e.g., CountSketch, quantization) and adaptive optimizers (e.g., Adam, AMSGrad) is a desirable goal in federated learning (FL), with potentia…
cs.LG2025
Optimization and Generalization Guarantees for Weight Normalization
Pedro Cisneros-Velarde, Zhijie Chen, Sanmi Koyejo +1
Weight normalization (WeightNorm) is widely used in practice for the training of deep neural networks and modern deep learning libraries have built-in implementations of it. In thi…