Paper Reading: The Future of Large Language Model Pre-training is Federated

Session led by Mike A

Is the future of model pre-training decentralized?

Join us for a captivating session as we explore the cutting-edge breakthrough of federated learning in language model pre-training, featuring an in-depth discussion of the "The Future of Large Language Model Pre-training is Federated" paper!

This paper reading event will shed light on how federated learning (FL) could be set to revolutionize the training of large language models (LLMs). Discover how FL could mobilize vast, underutilized data and computational resources from across the globe, enabling large-scale collaboration and democratizing the pre-training of LLMs. Learn about the innovative approach that allows organizations, and hopefully, soon individuals, to collectively train LLMs using limited resources while matching or even surpassing the performance of traditional centralized models!

This event is a must-attend for AI researchers, software developers, data scientists, and tech enthusiasts eager to grasp the future of LLM pre-training and its implications for AI advancements. Whether you're interested in scaling AI models, leveraging federated learning for collaborative training, or simply curious about the next big thing in AI technology, this paper reading offers invaluable insights and discussions on some of the latest AI research.

Mark your calendars and join us for an exciting hour of "Federated Learning: The Future of Large Language Model Pre-training" exploration!

Reference Links:
https://flower.ai/
https://github.com/adap/flower