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cs.LG2025
Memory-adaptive Depth-wise Heterogeneous Federated Learning
Kai Zhang, Yutong Dai, Hongyi Wang +3
Federated learning is a promising paradigm that allows multiple clients to collaboratively train a model without sharing the local data. However, the presence of heterogeneous devi…
cs.LG2024
Maestro: Uncovering Low-Rank Structures via Trainable Decomposition
Samuel Horvath, Stefanos Laskaridis, Shashank Rajput +1
Deep Neural Networks (DNNs) have been a large driver for AI breakthroughs in recent years. However, these models have been getting increasingly large as they become more accurate a…