Communication-Efficient Federated Learning

Brief Description

A groundbreaking algorithm that significantly reduces communication time and message size in distributed machine learning, ensuring fast and reliable model convergence.

Full Description

This technology presents a novel approach to federated learning that addresses the critical challenge of communication bottlenecks by transmitting only a single scalar value instead of high-dimensional parameter sets during the model update phase. This method dramatically lowers bandwidth requirements and communication time, facilitating scalable, efficient, and privacy-preserving machine learning across distributed networks.

Suggested uses

• Scalable federated learning solutions for Internet of Things (IoT) devices and mobile applications. 
• Efficient distributed learning systems for edge computing environments. 
• Resource-constrained scenarios requiring minimal data transmission and low energy consumption.

Advantages

• Drastic reduction in data transmitted, minimizing communication time and bandwidth usage. 
• Guaranteed convergence for reliable and efficient model updates. 
• Significant scalability improvements allowing for more devices to participate in federated learning. 
• Potential for enhanced security and privacy through gradient obfuscation. 
• Simple and robust encoding process for facile integration into existing systems.

Patent Status

Patent Pending

Related Materials

• Rostami, M., Kia, S. S. (2024). FedScalar: A Communication efficient Federated Learning. arXiv.