Authors: Yun-Wei Chu, Dong-Jun Han, Seyyedali Hosseinalipour, Christopher G. Brinton

Published on: February 03, 2024

Impact Score: 8.15

Arxiv code: Arxiv:2402.02225

Summary

• What is new: Introduction of CoPreFL, a collaborative pre-training approach designed for better initializations in federated learning tasks.
• Why this is important: Most federated learning methodologies start from a randomly initialized model, which may not be optimal.
• What the research proposes: A meta-learning based pre-training algorithm, CoPreFL, that adapts to any downstream federated learning task by mimicking distributed scenarios and focusing on both average performance and fairness.
• Results: CoPreFL provides robust initializations that enhance average performance and fairness in unseen downstream federated learning tasks.

Technical Details

Technological frameworks used: CoPreFL, a collaborative pre-training approach based on meta-learning

Models used: Federated learning models

Data used: Distributed scenarios simulations

Potential Impact

Sectors relying on federated learning models, such as healthcare, finance, and IoT, could see improvements in model performance and fairness.

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