This editorial introduces four papers in Volume 2, Issue 3 of the Journal of Reliable and Secure Computing, covering trustworthy AI governance, secure edge computing, federated fine-tuning, and outsourced LoRA adaptation. More >

Edge nodes are among the most resource-constrained components of computing infrastructure, yet security verification competes directly with application execution for limited compute resources. In Named Data Networking (NDN), each retrieved object carries a producer signature whose verification consumes edge CPU cycles, while existing cryptographic and orchestration studies rarely quantify its impact on service reliability. This paper develops SLED, a secure edge--cloud framework over NDN that incorporates a Security Verification Engine (SVE) into the edge compute budget, explicitly coupling verification occupancy with named-function execution. SLED combines DACSIR, which prices verification... More >

Federated fine-tuning adapts language models to distributed data and is widely adopted as a privacy-preserving alternative to centralized training, yet constrained clients must still store model weights and training states, execute updates, and communicate with a server. This review examines four composable routes---parameter-efficient and quantized adaptation, backpropagation-free adaptation, proxy or submodel adaptation, and split federated adaptation---through a common framework that traces the objects each endpoint retains, exchanges, and discloses. When a client retains the complete base, reducing adapter state leaves a base-storage floor; boundary communication depends on input dimensi... More >

Outsourced model fine-tuning requires reliable task allocation, result validation, and incentives for quality. We present TrustCompute, a compute-sharing platform integrating two-stage adapter verification, reputation-aware scheduling, quality-based payments, and auditable ledger records for LoRA fine-tuning. Our evaluation combines synthetic workloads, real GPU-based fine-tuning, and a local Ethereum Virtual Machine (EVM) environment. Across 440 validation cases, all 400 invalid submissions were rejected and all 40 valid submissions were accepted. With 50% unreliable workers, reputation-aware scheduling achieved 100% task completion and reduced the mean failed-attempt time by 89% relative t... More >

Artificial intelligence is becoming a core engine of corporate digital transformation, but its value depends first on secure, reliable, and accountable data and model infrastructures. As firms combine cloud platforms, edge devices, IoT sensors, digital twins, platform data, and algorithmic decision systems, they also expand the attack surface, privacy exposure, model security risk, and compliance burden. This paper develops a security-aware data and AI governance framework for AI-driven corporate digital transformation. It positions the framework as a unified governance model rather than a narrow extension of data management: data governance controls data classification, provenance, access,... More >