The proliferation of Internet of Things (IoT) devices and their vulnerability to malware infections pose critical security challenges in IoT networks and multi-access edge computing (MEC). Traditional federated learning-based IoT malware detection (FL-IMD) methods face limitations in privacy, reliability, and client authentication, necessitating innovative solutions. This study proposes a reliable and privacy-preserving federated learning framework (RPFL) that integrates elliptic curve digital signature algorithm (ECDSA), homomorphic encryption and blockchain technology to enhance privacy, reliability, and client verification in FL-IMD. To address challenges with fully homomorphic encryption (FHE), particularly its reliance on an external aggregator, we introduce two smart contract-based schemes: one to incentivize client participation and another to mitigate aggregator failures. Experimental results on the N-BaIoT dataset show that RPFL achieves IoT malware detection accuracy comparable to state-of-the-art methods, while significantly enhancing reliability and privacy in the aggregation process. Furthermore, our blockchain integration outperforms the prominent blockchain-based FL framework, BCFL, by reducing communication costs and latency. These findings highlight the potential of RPFL to advance privacy-preserving, reliable, and secure FL-based IMD in IoT networks and MEC environments.

Wireless sensor networks (WSNs) are mainly data-driven networks adopted to improve the Internet of Things (IoT) in terms of data throughput, energy-efficiency and self-management. Improving the data lifespan of WSN impacts the performance of the IoT. Achieving data reliability in applications of WSNs deployed in harsh environments is challenging due to the extreme constraints in resources of sensor nodes (SNs). Motivated by the inexpensive infrastructure of WSNs, a number of distributed storage systems have been proposed focusing on achieving data Survivability rather than network reliability. In this paper, we focus on data storage at the things layer, (wireless sensors). We evaluate the performance of a number of distributed data storage systems over WSN running over the Zigbee MAC protocol. Based on our findings, we introduce a new efficient-energy data dissemination scheme called Data Survivability with Energy Efficiency (DSwEE) that outperforms existing schemes. We compare DSwEE against two prominent protocols in data storage, namely: Decentralized Erasure Code for Data Survivability (DEC-DS) and Decentralized Erasure Code Encode-and-Disseminate (DEC-EaD). Results show that DSwEE achieves better performance than both of DEC-DS and DEC-EaD in terms of the energy consumption and data recoverability for localized failures, which improves the lifespan of the network.

Botnet attacks on Internet of Things (IoT) devices are escalating at the 5G/6G multi-access edge, yet most federated learning frameworks for IoT malware detection (FL-IMD) still hinge on a central aggregator, enlarging the attack surface, weakening privacy, and creating a single point of failure. We propose a two-tier, fully decentralized FL architecture aligned with MEC’s Proximal Edge Server (PES)/Supplementary Edge Server (SES) hierarchy. PES nodes train locally and encrypt updates with the Cheon–Kim–Kim–Song (CKKS) scheme; SES nodes verify ECDSA-signed provenance, homomorphically aggregate ciphertexts, and finalize each round via an Algorand-style committee that writes a compact, tamper-evident record (update digests/URIs and a global-model hash) to an append-only ledger. Using the N-BaIoT benchmark with an unsupervised autoencoder, we evaluate known-device and leave-one-device-out regimes against a classical centralized baseline and a cryptographically hardened but server-centric variant. With the heavier CKKS profile, attack sensitivity is preserved (TPR $\ge 0.99$), and specificity (TNR) declines by only 0.20 percentage points relative to plaintext in both regimes; a lighter profile maintains TPR while trading 3.5–4.8 percentage points of TNR for about 71% smaller payloads. Decentralization adds only a negligible per-round overhead for committee finality, while homomorphic aggregation dominates latency. Overall, our FL-IMD design removes the trusted aggregator and provides verifiable, ledger-backed provenance suitable for trustless MEC deployments.