federated learning

FedCAMP-IDS: a federated cluster-aware memory-augmented prototypical network for intrusion detection in heterogeneous IoT environments

This paper proposes FedCAMP-IDS, a Federated Cluster-Aware Memory-Augmented Prototypical Network for privacy-preserving intrusion detection in distributed network environments, and integrates Cluster-Aware Contrastive Pretraining, memory-augmented few-shot prototypical learning, adaptive prototype mixing, and Extreme V...

Failure Propagation and Self-Correction in Multi-Agent LLM Systems: How Deliberative Consensus, Credit Assignment, and Architectural Isolation Jointly Determine Systemic Reliability

This version corrects the use of a withdrawn preprint. Version 2 cited the CONCAT framework (arXiv:2605.29612) in Section 3.4 and reported its results of up to 2.02x higher efficiency and a 50.1% latency reduction. On 2026-09-22 its authors withdrew the manuscript, writing: "We identified a potential issue in the repea...

AutoFL: System and Benchmark Suite

Source code, benchmark suite, and generated client files for the paper Schema-Augmented LLM Prompting for Converting ML Training Scripts to Federated Learning Clients (ACM TOSEM, resubmission 2026). v2.0 corrects FedAvg to sample-count weighting, partitions the IID simulation, moves generated-module import to preflight...

Modeling Imbalanced Financial Transactions for Credit Card Fraud Detection Using Machine Learning and Deep Learning Techniques

This review aims at presenting a comprehensive review of the advanced techniques in the field of credit card fraud detection with special emphasis on the evolution of advanced deep learning techniques from conventional machine learning techniques in the time frame of 2023-2025.

Digital Twin-Based Simulation of Electric VehicleInteraction within Smart City Energy Networks

This work provides a comprehensive roadmap for developing intelligent, sustainable and resilient EV charging ecosystems that will resolve technical, economical, and ecologicalconsiderations while protecting the privacy of data and securing the system.

Deepfake-as-a-Service: Governance Frameworks, Software Quality Engineering, and Organizational Resilience in the Age of Synthetic Media

*** PREPRINT / AUTHOR-ACCEPTED VERSION ***This paper was presented at the conference and is the author-accepted camera-ready version. It is posted here for self-archiving purposes in accordance with the IEEE Author Posting Policy prior to official publication and indexing in IEEE Xplore. Abstract—Deepfake-as-a-Service...

Correctness Debt at the Execution Boundary: How Type-System Gaps, Semantic Ambiguity, Harness Lifecycle Debt, Format Divergence, and Verification Theater Jointly Define a Structural Deficit in Deployed Software Infrastructure

This version (2026-09-26) corrects a citation error found by an automated check and confirmed by hand against the arXiv abstracts. Version 2 cited the identifier 2607.07314, an unrelated federated-learning paper posted after this synthesis was drafted, for the quantum software debugging paper that notes quantum bugs "o...

Communication-Efficient Federated Learning via Fractional-Order Gradient Descent With Adaptive Momentum Under Non-IID Data

Federated learning (FL) enables collaborative model training without centralized data sharing, yet its practical deployment is often hindered by slow convergence and excessive communication overhead, particularly under non-Independent and non-Identically Distributed (non-IID) data distributions. To address these challe...

Trust-Aware Federated Deep Learning for Network Anomaly Detection Under Non-IID Data

Distributed network systems produce complex traffic flows that are hard to analyze with a centralized architecture of Intrusion Detection Systems. Traffic distributions are different between participating edge nodes, and it can involve sharing sensitive traffic information and require more communication. A trust-aware...

Noise Placement, Privacy Accounting, and Structured Clipping in Client-Level Differentially Private Federated Learning An Empirical Study

This paper presents a controlled empirical study of three implementation choices in client-level differentially private federated learning: noise placement, privacy accounting, and structured clipping. Experiments are conducted under a trusted-server threat model with client-level add/remove adjacency across CIFAR-10,...

LESA: Lyapunov-controlled wireless federated learning with adaptive straggler mitigation

Equivalent on Accuracy, Ordered in Fidelity: Aggregation Under Partial Reception in Decentralized Federated Learning