Categories: FAANG

Momentum Approximation in Asynchronous Private Federated Learning

This paper was accepted for presentation at the International Workshop on Federated Foundation Models (FL@FM-NeurIPS’24), held in conjunction with NeurIPS 2024.
Asynchronous protocols have been shown to improve the scalability of federated learning (FL) with a massive number of clients. Meanwhile, momentum-based methods can achieve the best model quality in synchronous FL. However, naively applying momentum in asynchronous FL algorithms leads to slower convergence and degraded model performance. It is still unclear how to effective combinie these two techniques together to achieve a win-win…
AI Generated Robotic Content

Recent Posts

Are there any open source alternatives to this?

I know there are models available that can fill in or edit parts, but I'm…

7 hours ago

The future of engineering belongs to those who build with AI, not without it

As we look ahead, the relationship between engineers and AI systems will likely evolve from…

8 hours ago

The 8 Best Handheld Vacuums, Tested and Reviewed (2025)

Lightweight, powerful, and generally inexpensive, the handheld vacuum is the perfect household helper.

8 hours ago

I really miss the SD 1.5 days

submitted by /u/Dwanvea [link] [comments]

1 day ago

Latent Bridge Matching: Jasper’s Game-Changing Approach to Image Translation

Discover how latent bridge matching, pioneered by the Jasper research team, transforms image-to-image translation with…

1 day ago

A Gentle Introduction to SHAP for Tree-Based Models

Machine learning models have become increasingly sophisticated, but this complexity often comes at the cost…

1 day ago