federated learning

Beyond Leave-One-Out: Private and robust contribution evaluation in Federated Learning

Communication-Efficient-Federated-Optimization-via-M-ALO

Implementation of M-ALO1 and M-ALO2 — two enhanced Ant Lion Optimizer variants for communication-efficient Federated Learning

Precision and Coverage in Robust Federated Learning: Code and Reproducibility Evidence

This deposit provides the software and reproducibility evidence for Precision and Coverage in Robust Federated Learning. It includes compressed client-update codecs, robust aggregation, seven development candidates and a complete five-seed comparison of three methods. Archived checkpoints, predictions, confusion matric...

Source Code for Federated Edge Learning over Optical-UAV Access Links with Deterministic-Payload Compression and Packetized Scheduling

This code-only package accompanies the manuscript “Federated Edge Learning over Optical-UAV Access Links With Deterministic-Payload Compression and Packetized Scheduling.”The software implements static mixed-resolution compression with error feedback (SMR-EF) and PacketFair scheduling for simulated federated learning o...

Spatio-Temporal Graph Transformer MARL for Resilient Point of Common Coupling Control in Networked Grid Tied Micro-grids

The increasing penetration of renewable energy sources introduces significant variability and uncertainty into grid-connected microgrids (MGs), affecting voltage stability, frequency regulation, power quality, fault ride-through capability, and operational resilience at the Point of Common Coupling (PCC). These challen...

"Classical vs. AI-Based Methods in Autonomous Vehicles - Reproducible Experiments from Perception to Field Safety Data"

"AV-Stack-Eval is a reproducible research dataset for comparing classical and AI-based methods across the autonomous vehicle (AV) software stack. It packages the inputs, source code, per-replicate results, summary statistics, tables and figures of twelve controlled experiments (E1\u2013E12). The experiments cover perce...

FedREST-HIDS: split-federated intrusion detection for resource-heterogeneous IIoT

Code accompanying "FedREST-HIDS: Split-Federated Intrusion Detection for Resource-Heterogeneous Industrial IoT" (Vyas, Hwang & Lin), submitted to Computer Communications. FedREST-HIDS partitions intrusion detection across three tiers. Resource-rich devices train complete local models and take part in federated learning...

FedREST-HIDS: split-federated intrusion detection for resource-heterogeneous IIoT

Code accompanying "FedREST-HIDS: Split-Federated Intrusion Detection for Resource-Heterogeneous Industrial IoT" (Vyas, Hwang & Lin), submitted to Computer Communications. FedREST-HIDS partitions intrusion detection across three tiers. Resource-rich devices train complete local models and take part in federated learning...

Federated temporal-aware lightweight intrusion detection system for CAN-based in-vehicle networks: A hybrid deep learning and privacy-preserving framework

A Federated Temporal-Aware Lightweight IDS (FTL-IDS) that co-designs multi-domain CAN feature extraction, an LSTM–Transformer temporal encoder, 8-bit quantization-aware training (QAT), and federated learning (FL).

Spatio-Temporal Graph Transformer MARL for Resilient Point of Common Coupling Control in Networked Grid Tied Micro-grids

The increasing penetration of renewable energy sources introduces significant variability and uncertainty into grid-connected microgrids (MGs), affecting voltage stability, frequency regulation, power quality, fault ride-through capability, and operational resilience at the Point of Common Coupling (PCC). These challen...

Communication-Efficient-Federated-Optimization-via-M-ALO

Implementation of M-ALO1 and M-ALO2 — two enhanced Ant Lion Optimizer variants for communication-efficient Federated Learning

Precision and Coverage in Robust Federated Learning: Code and Reproducibility Evidence

This deposit provides the software and reproducibility evidence for Precision and Coverage in Robust Federated Learning. It includes compressed client-update codecs, robust aggregation, seven development candidates and a complete five-seed comparison of three methods. Archived checkpoints, predictions, confusion matric...