The field of edge computing and distributed learning is rapidly evolving, with a focus on developing innovative solutions to address the challenges of real-time data processing, privacy preservation, and energy efficiency. Researchers are exploring new architectures and algorithms to enable efficient data processing and analysis at the edge, while minimizing latency and improving overall system performance. Notably, split computing and distributed beamforming are emerging as key technologies to enhance the age of information and reduce energy consumption in IoT networks. Furthermore, adaptive model partitioning and transfer learning are being investigated to improve the accuracy and efficiency of edge-based machine learning models. Overall, the field is moving towards more decentralized, autonomous, and adaptive systems that can operate effectively in dynamic and resource-constrained environments. Noteworthy papers include: The paper on Multi-AAV-enabled Distributed Beamforming in Low-Altitude Wireless Networking for AoI-Sensitive IoT Data Forwarding, which proposes a novel distributed beamforming approach to enhance the age of information in IoT networks. The paper on Adaptive AI Model Partitioning over 5G Networks, which develops an adaptive model partitioning scheme to optimize the trade-off between latency, energy consumption, and privacy in 5G networks.