Google llc (20240249193). Heterogeneous Federated Learning Via Multi-Directional Knowledge Distillation simplified abstract

Heterogeneous Federated Learning Via Multi-Directional Knowledge Distillation

Organization Name

google llc

Inventor(s)

Jared Alexander Lichtarge of Brooklyn NY (US)

Rajiv Mathews of Sunnyvale CA (US)

Rohan Anil of Lafayette CA (US)

Ehsan Amid of Mountain View CA (US)

Shankar Kumar of New York NY (US)

Heterogeneous Federated Learning Via Multi-Directional Knowledge Distillation - A simplified explanation of the abstract

This abstract first appeared for US patent application 20240249193 titled 'Heterogeneous Federated Learning Via Multi-Directional Knowledge Distillation

Simplified Explanation

The patent application is about an enhanced federated learning method that utilizes clients with varying computational resources to improve performance and convergence speed. By distilling knowledge between server models produced by different client pools, information is shared without sharing model parameters, leading to better results in fewer rounds.

• Multi-directional knowledge distillation between server models
• Use of unlabeled server data as distillation dataset
• Co-distilling models from different client pools
• Sharing information without sharing model parameters
• Increased performance and faster convergence

Key Features and Innovation

• Enhanced federated learning method
• Utilizes clients with varying computational resources
• Multi-directional knowledge distillation between server models
• Use of unlabeled server data for distillation
• Co-distillation of models from different client pools
• Information sharing without sharing model parameters
• Improved performance and faster convergence

Potential Applications

This technology can be applied in various fields such as healthcare, finance, and telecommunications for collaborative machine learning tasks where data privacy is a concern.

Problems Solved

This technology addresses the limitations of conventional federated learning methods by improving performance and convergence speed when using clients with varying computational resources.

Benefits

• Increased performance in federated learning tasks
• Faster convergence in fewer rounds
• Enhanced data privacy by not sharing model parameters
• Improved collaboration between clients with varying resources

Commercial Applications

• Healthcare: Collaborative medical research while protecting patient data
• Finance: Secure collaborative financial analysis without sharing sensitive information
• Telecommunications: Enhanced network optimization without compromising user privacy

Questions about Enhanced Federated Learning

How does multi-directional knowledge distillation improve federated learning performance?

Multi-directional knowledge distillation allows for information sharing between server models without sharing sensitive model parameters, leading to enhanced performance and faster convergence.

What are the potential applications of this technology beyond federated learning?

This technology can be applied in various industries such as healthcare, finance, and telecommunications for collaborative machine learning tasks where data privacy is crucial.

Original Abstract Submitted

generally, the present disclosure is directed to enhanced federated learning (fl) that employs a set of clients with varying amounts of computational resources (e.g., system memory, storage, and processing bandwidth). to overcome limitations of conventional fl methods that employ a set of clients with varying amounts of computational resources, the embodiments run multi-directional knowledge distillation between the server models produced by each federated averaging (fedavg) pool, using unlabeled server data as the distillation dataset. by co-distilling the two (or more) models frequently over the course of fedavg rounds, information is shared between the pools without sharing model parameters. this leads to increased performance and faster convergence (in fewer federated rounds).