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 18417947 titled 'Heterogeneous Federated Learning Via Multi-Directional Knowledge Distillation

Simplified Explanation: The patent application discusses an enhanced federated learning method that utilizes clients with varying computational resources to improve performance and convergence speed.

**Multi-directional knowledge distillation:** Distilling knowledge between server models produced by different client pools without sharing model parameters.
**Co-distillation:** Sharing information between client pools by distilling models frequently during the federated averaging rounds.
**Utilizing unlabeled server data:** Using unlabeled data for distillation to enhance performance and convergence speed.

Key Features and Innovation:

Enhanced federated learning method
Utilizes clients with varying computational resources
Multi-directional knowledge distillation between server models
Co-distillation of models to share information without sharing parameters
Utilizes unlabeled server data for distillation

Potential Applications:

Machine learning
Artificial intelligence
Data analysis

Problems Solved:

Overcoming limitations of conventional federated learning methods
Improving performance and convergence speed
Sharing information between client pools effectively

Benefits:

Increased performance
Faster convergence
Efficient sharing of information

Commercial Applications: Enhanced federated learning methods can be applied in various industries such as healthcare, finance, and e-commerce to improve data analysis and machine learning models.

Prior Art: Further research can be conducted in the field of federated learning and knowledge distillation to explore similar methods and technologies.

Frequently Updated Research: Stay updated on advancements in federated learning, knowledge distillation, and machine learning techniques to enhance the efficiency of data analysis and model training.

Questions about Enhanced Federated Learning: 1. How does multi-directional knowledge distillation improve federated learning performance? 2. What are the potential implications of co-distillation in federated averaging rounds?

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).