How can I build efficient, personalized distributed models with heterogeneous data?
Communication-Efficient Personalized Distributed Learning with Data and Node Heterogeneity
This paper proposes a communication-efficient method for training personalized machine learning models in a decentralized multi-agent system, addressing both data and node heterogeneity (different data distributions and device capabilities). The core idea is based on the "distributed strong lottery ticket hypothesis" (DSLTH), which posits that a large, randomly initialized neural network contains multiple, diverse subnetworks that can be effectively trained for individual agents. Instead of training all model parameters, the method focuses on learning and exchanging binary "masks" that select active parts of the network for each agent, while shared model parameters remain fixed. This dramatically reduces communication costs, crucial for real-world multi-agent applications.
For LLM-based multi-agent systems, this research is relevant because it offers a potential solution to the challenge of efficiently personalizing large language models in a distributed setting. The mask-based approach could enable agents with varying resources to adapt a shared LLM to their specific data and tasks, while minimizing communication overhead. The DSLTH suggests that even with limited communication and diverse agent capabilities, effective personalized models can be learned collaboratively.