How to train LLMs for distributed multi-task learning?
Distributed Networked Multi-task Learning
October 7, 2024
https://arxiv.org/pdf/2410.03403- Develops a decentralized algorithm for training multiple related machine learning models (multi-task learning) across a network of nodes, particularly useful when datasets are heterogeneous and correlated.
- Nodes, potentially representing individual LLMs, can learn collaboratively without sharing raw data, preserving privacy while benefiting from diverse knowledge distributed across the network.
- Addresses the challenge of inferring relationships between different learning tasks (e.g., different LLMs specializing in specific domains) through a dynamic estimation of task relationships.
- Employs a two-timescale approach, allowing for frequent local updates within groups of similar LLMs and less frequent global updates for efficient communication and robust learning in dynamic environments.