Abstract:
The Internet of things (IoT) presents unique challenges for the deployment of machine
learning (ML) models, particularly due to constraints on computational resources, the necessity for
decentralized processing, and concerns regarding security and privacy in interconnected environments such as the Internet of cloud. In this paper, a novel decentralized ML framework is proposed
for IoT environments characterized by wireless communication, dynamic data streams, and integration with cloud services. The framework integrates incremental learning algorithms with a robust
decentralized model exchange protocol, ensuring that data privacy is preserved, while enabling
IoT devices to participate in collaborative learning from distributed data across cloud networks.
By incorporating a gossip-based communication protocol, the framework ensures energy-efficient,
scalable, and secure model exchange, fostering effective knowledge sharing among devices, while
addressing the potential security threats inherent in cloud-based IoT ecosystems. The framework’s
performance was evaluated through simulations, demonstrating its ability to handle the complexities
of real-time data processing in resource-constrained IoT environments, while also mitigating security
and privacy risks within the Internet of cloud.
