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Related Experiment Video

Updated: Sep 11, 2025

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

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FedLSC: Improving Communication Efficiency and Robustness in Federated Learning With Stragglers and Adversaries.

Hyeong-Gun Joo, Songnam Hong, Dong-Joon Shin

IEEE Transactions on Neural Networks and Learning Systems

|August 18, 2025

Summary

This summary is machine-generated.

This study introduces FedLSC, a federated learning (FL) framework enhancing efficiency and robustness without public data. FedLSC significantly cuts communication costs, making FL more practical for real-world applications.

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Area of Science:

Artificial Intelligence
Machine Learning
Distributed Systems

Background:

Federated learning (FL) faces challenges like stragglers, adversaries, and high communication costs.
Existing FL methods often require public data, limiting real-world applicability and resilience.

Purpose of the Study:

To propose FedLSC, a novel FL framework designed to improve robustness and efficiency.
To address limitations of current FL approaches by eliminating reliance on public data during training.

Main Methods:

FedLSC utilizes layer-selected correlation (LSC) for enhanced robustness and efficiency.
Key innovations include layer selection (LS) for reduced communication, LS-based scaled sign-stochastic gradient descent (SSS) for local updates, and LSC-based aggregation.
The SSS scheme mitigates quantization loss and communication overhead.

Main Results:

FedLSC significantly reduces communication costs, achieving as low as 0.01% of state-of-the-art methods.
The framework maintains performance while drastically cutting communication needs.
Evaluations demonstrate robust performance and efficiency in bandwidth-constrained FL scenarios.

Conclusions:

FedLSC offers a practical and resilient solution for modern federated learning applications.
The framework effectively enhances both the efficiency and robustness of FL systems.
FedLSC proves particularly beneficial in environments with limited communication bandwidth.