ArticleSensors (Basel, Switzerland)2026
A Federated Multi-Agent Communication Framework for Autonomous Cyber Defense in Edge-Cloud IoT Environments.
Article in Sensors (Basel, Switzerland), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
1 author.
Funding
No grant is acknowledged in the PubMed record.
Abstract
With increasing system complexity and attack sophistication, autonomous cyber defense has become essential in edge-cloud Internet of Things (IoT) ecosystems, requiring intelligent, secure, and scalable agent communication frameworks. This paper proposes FMAD (Federated Multi-Agent Defense), a conceptual federated multi-agent framework that enables intelligent agent communication for autonomous cyber defense through collaborative sensing and federated learning, with a design emphasis on security and privacy preservation. The proposed framework defines a set of specialized intelligent agents responsible for distributed traffic sensing, threat intelligence analysis, federated model optimization, secure agent-to-agent communication, and adaptive mitigation. As an initial proof of concept, the Threat Intelligence Agent (TIA) was implemented using an xLSTM-based temporal intrusion detection model developed in this study and trained on the CIC-IoT-2023 dataset, achieving a detection accuracy of 99.89% while efficiently identifying temporal traffic anomalies at edge nodes. The FMAD framework integrates multiple collaborative agents for secure communication, federated learning, and coordinated cyber defense in edge-cloud IoT environments. The experimental evaluation focuses on the xLSTM-based Threat Intelligence Agent (TIA) for DDoS detection, while the remaining components are assessed at the design level. However, empirical validation in the current study is limited solely to the TIA. To support trustworthy collaboration among distributed agents, the framework further proposes a secure communication architecture based on Elliptic Curve Cryptography (ECC) key agreement, ChaCha20-Poly1305 authenticated encryption, and a permissioned blockchain ledger, which are designed to provide secure interaction management and traceability within the stated security assumptions. This federated multi-agent approach provides a promising foundation for intelligent agent communication and scalable autonomous cyber defense in real-world edge-cloud IoT environments.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.