arXiv subjects

Nikos Papadis

Publications and source records attributed to Nikos Papadis.

3 recordsLinked to original sources

arXiv2026

Assurance-Scoped Reliability for Agentic Networks: Capturing the State That Matters

Agentic networks transform accepted intents into operational services through autonomous reasoning, adaptive planning, tool use, and cross-domain coordination, but these capabilities introduce failure modes that conventional reliability measures do not fully capture. An accepted intent may still be carried out incorrectly, for example because the system acts on stale information, repeats an external action, applies only part of a change, or enters a fallback mode that quietly relaxes policy enforcement. Such failures can leave a service running and apparently healthy while its behavior is unsafe or unaccountable, with too little evidence to detect, explain, or recover from them. This article proposes Reliability Assurance Intelligence (RAI), a general assurance architecture for such systems. From the service description, RAI derives a per-service reliability profile that states what must be checked, recorded, recovered, and audited. At runtime, generic functions use the service profile to retain the durable state needed for recovery and accountability in a context capsule adapted to service conditions. Using an agentic lifecycle manager for deterministic network services as a running example, we design the RAI architecture and propose a methodology for validating its reliability assurances.

cs.NI↗

arXiv2026

CLAD: A Clustered Label-Agnostic Federated Learning Framework for Joint Anomaly Detection and Attack Classification

The rapid expansion of the Internet of Things (IoT) and Industrial IoT (IIoT) has created a massive, heterogeneous attack surface that challenges traditional network security mechanisms. While Federated Learning (FL) offers a privacy-preserving alternative to centralized Intrusion Detection Systems (IDS), standard approaches struggle to generalize across diverse device behaviors and typically fail to utilize the vast amounts of unlabeled data present in realistic edge environments. To bridge these gaps, we propose CLAD, a holistic framework that seamlessly incorporates Clustered Federated Learning (CFL) with a novel Dual-Mode Micro-Architecture ($\text{DM}^2\text{A}$). This unified approach simultaneously tackles the two primary bottlenecks of IoT security: device heterogeneity and label scarcity. The $\text{DM}^2\text{A}$ component features a shared encoder followed by two branches, enabling joint unsupervised anomaly detection and supervised attack classification; this allows the framework to harvest intelligence from both labeled and unlabeled clients. Concurrently, the clustering component dynamically groups devices with congruent traffic patterns, preventing global model divergence. By carefully combining these elements, CLAD ensures that no data is discarded and distinct operational patterns are preserved. Extensive evaluations demonstrate that this integrated approach significantly outperforms state-of-the-art baselines, achieving a 30% relative improvement in detection performance in scenarios with 80% unlabeled clients, with only half the communication cost.

cs.LG↗

arXiv2025

CLoVE: Personalized Federated Learning through Clustering of Loss Vector Embeddings

We propose CLoVE (Clustering of Loss Vector Embeddings), a novel algorithm for Clustered Federated Learning (CFL). In CFL, clients are naturally grouped into clusters based on their data distribution. However, identifying these clusters is challenging, as client assignments are unknown. CLoVE utilizes client embeddings derived from model losses on client data, and leverages the insight that clients in the same cluster share similar loss values, while those in different clusters exhibit distinct loss patterns. Based on these embeddings, CLoVE is able to iteratively identify and separate clients from different clusters and optimize cluster-specific models through federated aggregation. Key advantages of CLoVE over existing CFL algorithms are (1) its simplicity, (2) its applicability to both supervised and unsupervised settings, and (3) the fact that it eliminates the need for near-optimal model initialization, which makes it more robust and better suited for real-world applications. We establish theoretical convergence bounds, showing that CLoVE can recover clusters accurately with high probability in a single round and converges exponentially fast to optimal models in a linear setting. Our comprehensive experiments comparing with a variety of both CFL and generic Personalized Federated Learning (PFL) algorithms on different types of datasets and an extensive array of non-IID settings demonstrate that CLoVE achieves highly accurate cluster recovery in just a few rounds of training, along with state-of-the-art model accuracy, across a variety of both supervised and unsupervised PFL tasks.

cs.LG↗