positions

DC1: Sparse on-device training
DC2: Adaptive sensor- and context-aware learning
DC3: Battery-free Embedded AI
DC4: Split learning over distributed heterogeneous devices
DC5: Task-Agnostic Multimodal Foundation Models on Edge Devices
DC6: Efficient and realistic over-the-air-computing
DC7: Zero-power network pervasive intelligence
DC8: Secure explainable Embedded AI
DC9: Diagnosing Embedded AI Models
DC10: Embedded IoT Intelligence for Battery-free Swimming Milli-robots
DC11: Embedded active inference for autonomous robot manipulation
DC12: Split Learning across underwater and surface devices
DC13: 1-Bit Foundation Models for Smart Farming
DC14: Distributed inferencing on zero/low power embedded devices in smart farming
DC15: Continual on-tiny-device learning for dynamic environments
DC16: Networked scalable learning
DC17: Privacy in Embedded AI
DC18: Exploring uncertainty quantification of on-device learning for health