# FRL-FI: Transient Fault Analysis for Federated Reinforcement Learning-Based Navigation Systems

arXiv: https://arxiv.org/abs/2203.07276 · HTML: https://mlsanity.com/paper/2203.07276/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 13 of its 33 references that are in the map.
- First posted on arXiv 14 Mar 2022.

## Builds on (most cited first, 13 of 13)

- [Data Poisoning Attacks Against Federated Learning Systems](https://mlsanity.com/paper/2007.08432/) (2020)
- [Threats to Federated Learning: A Survey](https://mlsanity.com/paper/2003.02133/) (2020)
- [Robust Federated Learning with Noisy Communication](https://mlsanity.com/paper/1911.00251/) (2019)
- [A Decentralized Policy Gradient Approach to Multi-task Reinforcement Learning](https://mlsanity.com/paper/2006.04338/) (2020)
- [Autonomous Navigation via Deep Reinforcement Learning for Resource Constraint Edge Nodes using Transfer Learning](https://mlsanity.com/paper/1910.05547/) (2019)
- [ML-based Fault Injection for Autonomous Vehicles: A Case for Bayesian Fault Injection](https://mlsanity.com/paper/1907.01051/) (2019)
- [Multi-Task Federated Reinforcement Learning with Adversaries](https://mlsanity.com/paper/2103.06473/) (2021)
- [A Survey of FPGA-Based Robotic Computing](https://mlsanity.com/paper/2009.06034/) (2020)
- [Analyzing and Improving Fault Tolerance of Learning-Based Navigation Systems](https://mlsanity.com/paper/2111.04957/) (2021)
- [iELAS: An ELAS-Based Energy-Efficient Accelerator for Real-Time Stereo Matching on FPGA Platform](https://mlsanity.com/paper/2104.05112/) (2021)
- [MAVFI: An End-to-End Fault Analysis Framework with Anomaly Detection and Recovery for Micro Aerial Vehicles](https://mlsanity.com/paper/2105.12882/) (2021)
- [Circuit and System Technologies for Energy-Efficient Edge Robotics](https://mlsanity.com/paper/2202.11237/) (2022)
- [Roofline Model for UAVs:A Bottleneck Analysis Tool for Designing Compute Systems for Autonomous Drones](https://mlsanity.com/paper/2111.03792/) (2021)

## Abstract (arXiv)

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.