Towards Artificial General Intelligence (AGI) in the Internet of Things (IoT): Opportunities and Challenges

Artificial General Intelligence (AGI), possessing the capacity to comprehend, learn, and execute tasks with human cognitive abilities, engenders significant anticipation and intrigue across scientific, commercial, and societal arenas. This fascination extends particularly to the Internet of Things (IoT), a landscape characterized by the interconnection of countless devices, sensors, and systems, collectively gathering and sharing data to enable intelligent decision-making and automation. This research embarks on an exploration of the opportunities and challenges towards achieving AGI in the context of the IoT. Specifically, it starts by outlining the fundamental principles of IoT and the critical role of Artificial Intelligence (AI) in IoT systems. Subsequently, it delves into AGI fundamentals, culminating in the formulation of a conceptual framework for AGI's seamless integration within IoT. The application spectrum for AGI-infused IoT is broad, encompassing domains ranging from smart grids, residential environments, manufacturing, and transportation to environmental monitoring, agriculture, healthcare, and education. However, adapting AGI to resource-constrained IoT settings necessitates dedicated research efforts. Furthermore, the paper addresses constraints imposed by limited computing resources, intricacies associated with large-scale IoT communication, as well as the critical concerns pertaining to security and privacy.

Mobilizing Personalized Federated Learning via Random Walk Stochastic ADMM

In this research, we investigate the barriers associated with implementing Federated Learning (FL) in real-world scenarios, where a consistent connection between the central server and all clients cannot be maintained, and data distribution is heterogeneous. To address these challenges, we focus on mobilizing the federated setting, where the server moves between groups of adjacent clients to learn local models. Specifically, we propose a new algorithm, Random Walk Stochastic Alternating Direction Method of Multipliers (RWSADMM), capable of adapting to dynamic and ad-hoc network conditions as long as a sufficient number of connected clients are available for model training. In RWSADMM, the server walks randomly toward a group of clients. It formulates local proximity among adjacent clients based on hard inequality constraints instead of consensus updates to address data heterogeneity. Our proposed method is convergent, reduces communication costs, and enhances scalability by reducing the number of clients the central server needs to communicate with.