📢📱 ¡Atención a todos los amantes de la tecnología móvil en Ecuador! 🇪🇨 Este 2 de agosto a las 10:30 AM, los esperamos en el Auditorio de FIEC para un emocionante conversatorio sobre "Redes Celulares en Ecuador" 🌐. 💡 ¡Descubre los avances más recientes en el mundo de las redes celulares y cómo están transformando nuestra comunicación y conectividad! 🚀💬 No te quedes fuera de esta oportunidad única de aprender y compartir con expertos del campo. ¡Te esperamos! 🤝✨ Room: AUDITORIO DE FIEC, Bldg: 11A FIEC NUEVA, Via Perimetral Km 30.5, GUAYAQUIL, Guayas, Ecuador, 090501

In this talk we discuss approaches for distributed machine learning (ML) in wireless networks, in particular vehicular networks and resource-constrained Internet of Things (IoT) networks. Federated Learning (FL) and Split Learning (FL) are popular approaches in such environments. First, we present Early Exit of Communication (EEoC), which adaptively splits ML inference in an IoT edge computing environment to meet latency and energy constraints. This layer-based (vertically partitioned) approach has been extended by Distributed Micro-Split Deep Learning in Heterogeneous Dynamic IoT (DISNET), which adds horizontal partitioning to better support flexible, distributed, and parallel execution of neural network models on heterogeneous IoT devices under dynamic conditions. Then, we also consider the training aspect by developing and evaluating Adaptive REsource-aware Split-learning (ARES), a scheme for efficient model training in IoT systems. Recent work suggests Dynamic FL (DFL) for heterogeneous IoT, which uses resource-aware SL and FL based on similarity-based layer-wise model aggregation. Co-sponsored by: Universidad Tecnológica de Panamá Speaker(s): Torsten Braun Agenda: Registro de participantes Conferencia Preguntas y respuestas Despedida Room: Salón Roberto Barraza, Bldg: Edificio 1, Salón Roberto Barraza, Universidad Tecnológica de Panamá, Campus Víctor Levi Sasso, Panama, Panama, Panama