DECML: Distributed Edge Consensus Machine Learning Framework

The increasing reliance on interdependent datadriven services in the Internet of Things (IoT), smart homes, and Industry 4.0 is hindered by siloed security and privacy measures. Existing solutions like federated learning and distributed machine learning, with their various approaches such as differential privacy and homomorphic encryption, while promising, face challenges in ensuring robust security and privacy. We introduce Distributed Edge Consensus Machine Learning (DECML), a novel framework that enables secure, privacypreserving insights sharing among multiple stakeholders without exposing underlying data or models. DECML distributes queries and aggregates responses through independent nodes, achieving accuracy comparable to local deployments with minimal added latency. Our evaluation, using standard datasets and a 20-node network, demonstrates DECML's potential for collaborative decision-making without compromising privacy. This has significant implications for domains such as cybersecurity, healthcare, and financial services, where privacy concerns hinder data sharing.