FedResilience: A Federated Learning Application to Improve Resilience of Resource-Constrained Critical Infrastructures

Abstract

Critical infrastructures (e.g., energy and transportation systems) are essential lifelines for most modern sectors and have utmost significance in our daily lives. However, these important domains can fail to operate due to system failures or natural disasters. Though the major disturbances in such critical infrastructures are rare, the severity of such events calls for the development of effective resilience assessment strategies to mitigate relative losses. Traditional critical infrastructure resilience approaches consider that the available critical infrastructure agents are resource-sufficient and agree to exchange local data with the server and other agents. Such assumptions create two issues: (1) uncertainty in reaching convergence while applying learning strategies on resource-constrained critical infrastructure agents, and (2) a huge risk of privacy leakage. By understanding the pressing need to construct an effective resilience model for resource-constrained critical infrastructure, this paper aims at leveraging a distributed machine learning technique called Federated Learning (FL) to tackle an agent’s resource limitations effectively and at the same time keep the agent’s information private. Particularly, this paper is focused on predicting the probable outage and resource status of critical infrastructure agents without sharing any local data and carrying out the learning process even when most of the agents are incapable of accomplishing a given computational task. To that end, an FL algorithm is designed specifically for a resource-constrained critical infrastructure environment that could facilitate the training of each agent in a distributed fashion, restrict them from sharing their raw data with any other external entities (e.g., server, neighbor agents), choose proficient clients by analyzing their resources, and allow a partial amount of computation tasks to be performed by the resource-constrained agents. We considered a different number of agents with various stragglers and checked the performance of FedAvg and our proposed FedResilience algorithm with prediction tasks for a probable outage, as well as checking the agents’ resource-sharing scope. Our simulation results show that if the majority of the FL agents are stragglers and we drop them from the training process, then the agents learn very slowly and the overall model performance is negatively affected. We also demonstrate that the selection of proficient agents and allowing them to complete only parts of their tasks can significantly improve the knowledge of each agent by eliminating the straggler effects, and the global model convergence is accelerated.

Critical Infrastructures Overview: Past, Present and Future

“…To this end, Table 5 summarizes a classification of documents by year, type of publication and main focus. Analyzing in detail each of them, it emerges that Imteaj et al [51] has proposed a distributed machine learning technique called Federated Learning (FL) to predict the probable outage and resource status of CIs. Depina et al [52] investigates the application of the Performance-Based Wind Engineering (PBWE) methodology to the risk assessment of critical telecommunication infrastructure subjected to wind hazard.…”

Hierarchical Federated Learning for Edge-Aided Unmanned Aerial Vehicle Networks

“…This leads to a low participation rate of the UAVs, which will eventually affect the performance of the model [15]. Proposed methods that solve the system heterogeneity issues include using distributed learning to perform the training process across multiple edge devices [16,17], choosing the proficient devices by predicting outages and resource information of the critical infrastructure agents [18]. Even though most of the above-mentioned studies use FL as a base model, they do not consider extreme non-i.i.d.…”

Measuring Resilience in Smart Infrastructures: A Comprehensive Review of Metrics and Methods

“…External forces encompass natural disasters and extreme weather events (e.g., hurricanes, tornadoes, wind storms, ice storms), while internal forces include failures resulting from component malfunctions, system breakdowns, and human errors. This type of interdependence between smart infrastructures is referred to as "Smart Interdependent Critical Infrastructures (Smart ICIs) [10]". In this context, the primary focus of this paper is to elucidate the concept of resilience within the realm of Smart ICIs and to showcase the diverse resilience metrics and methodologies that researchers employ to evaluate the resilience of Smart ICIs.…”