FedSteer constructs a gradient subspace from cached client updates, projects active gradients to obtain coordinates, and reuses those coordinates on the drifted subspace to correct extreme staleness in federated learning.
arXiv preprint arXiv:2406.02877 , year=
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AlignFed introduces a multi-stage semantic alignment mechanism for asynchronous federated fine-tuning of LLMs to mitigate model drift, client drift, and aggregation unfairness in heterogeneous edge environments.
citing papers explorer
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FedSteer: Taming Extreme Gradient Staleness in Federated Learning with Corrective Projections and Caching
FedSteer constructs a gradient subspace from cached client updates, projects active gradients to obtain coordinates, and reuses those coordinates on the drifted subspace to correct extreme staleness in federated learning.
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AlignFed: Alignment-Aware Asynchronous Federated Fine-Tuning for Large Language Models in Heterogeneous Edge Environments
AlignFed introduces a multi-stage semantic alignment mechanism for asynchronous federated fine-tuning of LLMs to mitigate model drift, client drift, and aggregation unfairness in heterogeneous edge environments.