Presentation
RTFL: Energy-Aware Federated Learning for AIoT Design Via Adaptive Quantization-Based Multi-Agent Scheduling
DescriptionAlthough Federated Learning (FL) is becoming increasingly popular in designing Artificial Intelligence of Things (AIoT) applications, due to the varying computing and communication capabilities of resource-constrained devices, it suffers from the problems of slow convergence and poor training performance, especially when devices are powered by batteries. To address these issues, this paper introduces RTFL, a novel energy-aware Real-Time Federated Learning framework based on Multi-Agent Reinforcement Learning (MARL), aiming to enhance the knowledge sharing across AIoT devices within a specified training time constraint. Specifically, RTFL employs an Adaptive Quantization-based Multi-Agent Scheduling (AQMAS) strategy, enabling a team of agents to intelligently select devices with specific model quantization levels for each round of local training, taking into account the resource constraints (e.g., remaining battery power, computing capability, and communication bandwidth) of the current devices. By facilitating collaboration among agents through reinforcement learning, our approach enables devices to maximize their contributions to forming an optimal global model, while balancing the trade-off between the accuracy of quantized models and the limited resources available on each device. Comprehensive experiments show that RTFL not only accelerates the convergence of FL training, but also encourages devices to participate in more rounds of knowledge aggregation, thereby significantly improving overall training performance.
Event Type
Research Manuscript
TimeTuesday, July 2812:16pm - 12:30pm PDT
LocationMtg Room 202C