Robust Federated Learning for Detecting False Data Injection Attacks in Advanced Metering Infrastructure under Non-IID Data — Source Code

Abstract

This repository contains the complete source code for the paper "Robust Federated Learning for Detecting False Data Injection Attacks in Advanced Metering Infrastructure under Non-IID Data." The framework couples a lightweight two-branch GRU local detector with a server-side robust aggregation pipeline (per-client EMA smoothing, client-level trimmed filtering, and deviation-aware cosine weighting) to detect false data injection attacks (FDIA) in advanced metering infrastructure under non-IID household data and malicious-client poisoning. Contents: Data preparation: preprocess.py, ami_utils.py, select_households.py, partition_clients.py (ACORN-based non-IID partitioning), windowing.py, temporal_split.py (chronological train/validation/test split with embargo). Attack synthesis: synthesize_fdia.py (scaling, step, and gradual FDIA patterns under a 3-sigma stealth cap); poisoning.py (label flipping, Gaussian noise, parameter scaling, sign flipping, model replacement, min-max/min-sum, and intermittent poisoning). Model and federation: model_gru.py (two-branch GRU detector), fl_client.py and fl_server.py (robust aggregation), aggregators_baselines.py (FedAvg, Krum/Multi-Krum, coordinate-wise median, trimmed-mean, Bulyan, geometric-median/RFA, SCAFFOLD, FLTrust, Cos-Ref, FedCAP, SmartFL), metrics.py. Experiment drivers (one per table/figure): run_main.py, run_poisoning.py, run_ablation.py, run_beta_sweep.py, run_noniid_sweep.py, run_cross_dataset.py, run_scale_efficiency.py, orchestrated by experiment_runner.py. requirements.txt lists all dependencies; random seeds 2026–2030 reproduce the reported runs. Data availability: this deposit contains code only. The Low Carbon London and GoiEner smart-meter datasets are publicly available from their original providers; access instructions are given in the paper's Data Availability Statement.