How can UAVs share data for faster multi-task federated learning?
UAV-Assisted Multi-Task Federated Learning with Task Knowledge Sharing
January 22, 2025
https://arxiv.org/pdf/2501.10644This paper proposes a system for training multiple related machine learning models simultaneously using data collected by a swarm of UAVs and coordinated by multiple ground-based EVs, using a multi-task federated learning approach. This allows for efficient knowledge sharing by having the models share a common feature extractor while retaining task-specific layers. A task attention mechanism and optimal bandwidth allocation strategies are introduced to improve training speed and balance performance across tasks.
Relevant to LLM-based multi-agent systems are the concepts of:
- Multi-task learning with shared knowledge: This mirrors the potential for LLMs within agents to share underlying knowledge or components while specializing in individual tasks.
- Dynamic task weighting: The paper's task attention mechanism reflects how agent priorities could shift based on task performance and contribution, relevant to coordinating multiple LLM agents.
- Resource allocation and coordination: The focus on optimizing bandwidth allocation for UAVs translates to managing compute and communication resources for LLM agents in a distributed system.
- Coalition formation: The use of coalition formation games for UAV-EV association could be applicable to dynamic team formation in multi-agent LLM systems.