Client Selection for Federated Learning in Vehicular Edge Computing: A Deep Reinforcement Learning Approach
Vehicular edge computing (VEC) has emerged as a solution that places computing resources at the edge of the network to address resource management, service continuity, and scalability issues in dynamic vehicular environments. However, VEC faces challenges such as task offloading, varying communication conditions, and data security. To tackle thes...
Wearable sensor with Artificial Intelligence for prevention of falls in elderly people
Background: As per the census 2011, Disability is more common in elderly people. One of the major reasons for disability among the elderly people is falls. Every year, one-third of community-dwelling older adults (adults aged 65 and older) experience a fall. Falls, defined as “unexpected event[s] in which the participant comes to rest on the grou...
A MULTIPLE GRADIENT DESCENT DESIGN FOR MULTI-TASK LEARNING ON EDGE COMPUTING MULTI-OBJECTIVE MACHINE LEARNING APPROACH
In multi-task learning, multiple tasks are solved jointly, sharing inductive bias between them. Multi-task learning is inherently a multi-objective problem because different tasks may conflict, necessitating a trade-off. A common compromise is to optimize a proxy objective that minimizes a weighted linear combination of pertask losses. However, thi...
A DEEP LEARNING APPROACH FOR TASK OFFLOADING IN MULTI-UAV AIDED MOBILE EDGE COMPUTING
A number of electric devices in buildings can be considered as important demand response (DR) resources, for instance, the battery energy storage system (BESS) and the heat, ventilation, and air conditioning (HVAC) systems. The conventional model-based DR methods rely on efficient ondemand computing resources. However, the current buildings suffer ...
A DEEP LEARNING APPROACH FOR TASK OFFLOADING IN MULTI-UAV AIDED MOBILE EDGE COMPUTING
A number of electric devices in buildings can be considered as important demand response (DR) resources, for instance, the battery energy storage system (BESS) and the heat, ventilation, and air conditioning (HVAC) systems. The conventional model-based DR methods rely on efficient ondemand computing resources. However, the current buildings suffer ...
