FRL-FI: Transient Fault Analysis for Federated Reinforcement Learning-Based Navigation Systems
This addresses transient fault vulnerabilities in resource-constrained edge applications of federated reinforcement learning, such as drones and unmanned vehicles, with incremental improvements.
The paper experimentally evaluates the fault tolerance of federated reinforcement learning navigation systems under various conditions and proposes two cost-effective fault detection and recovery techniques, achieving up to 3.3x improvement in resilience with less than 2.7% overhead.
Swarm intelligence is being increasingly deployed in autonomous systems, such as drones and unmanned vehicles. Federated reinforcement learning (FRL), a key swarm intelligence paradigm where agents interact with their own environments and cooperatively learn a consensus policy while preserving privacy, has recently shown potential advantages and gained popularity. However, transient faults are increasing in the hardware system with continuous technology node scaling and can pose threats to FRL systems. Meanwhile, conventional redundancy-based protection methods are challenging to deploy on resource-constrained edge applications. In this paper, we experimentally evaluate the fault tolerance of FRL navigation systems at various scales with respect to fault models, fault locations, learning algorithms, layer types, communication intervals, and data types at both training and inference stages. We further propose two cost-effective fault detection and recovery techniques that can achieve up to 3.3x improvement in resilience with <2.7% overhead in FRL systems.