Fedimpro: Measuring and improving client update in federated learning

[PDF] arxiv.org

Pruner-Zero: Evolving Symbolic Pruning Metric from scratch for Large Language Models

P Dong, L Li, Z Tang, X Liu, X Pan, Q Wang… - arXiv preprint arXiv …, 2024 - arxiv.org
Despite the remarkable capabilities, Large Language Models (LLMs) face deployment
challenges due to their extensive size. Pruning methods drop a subset of weights to …
被引用次数：3 相关文章 所有 3 个版本
[PDF] acm.org

Bandwidth-Aware and Overlap-Weighted Compression for Communication-Efficient Federated Learning

Z Tang, J Huang, R Yan, Y Wang, Z Tang… - Proceedings of the 53rd …, 2024 - dl.acm.org
Current data compression methods, such as sparsification in Federated Averaging
(FedAvg), effectively enhance the communication efficiency of Federated Learning (FL) …
相关文章 所有 3 个版本
[PDF] arxiv.org

GAS: Generative Activation-Aided Asynchronous Split Federated Learning

J Yang, Y Liu - arXiv preprint arXiv:2409.01251, 2024 - arxiv.org
Split Federated Learning (SFL) splits and collaboratively trains a shared model between
clients and server, where clients transmit activations and client-side models to server for …
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