Federated Split Learning (FSL) has emerged as a transformative paradigm that synergizes the parallel processing and scalability of Federated Learning (FL) with the computational efficiency and enhanced privacy of Split Learning (SL). This comprehensive survey provides a systematic exploration of FSL, beginning with a detailed taxonomy of Distributed Machine Learning (DML) paradigms, tracing the progression from foundational concepts to advanced frameworks such as Federated Split Transfer Learning (FSTL) and Generalized Federated Split Transfer Learning (GFSTL). It then delves into the core challenges inherent to FSL, including privacy and security risks, system and data heterogeneity, computational and system constraints, communication overhead, and model optimization complexities. The heart of the survey presents a detailed categorization and analysis of FSL's diverse applications across key domains, including the Internet of Things (IoT) and Edge Computing (EC), wireless networks, healthcare, vehicular networks, Large Language Models (LLMs), and Earth Observation (EO). To ground this research, the survey further discusses standardized evaluation methodologies and implementation frameworks, followed by a quantitative visualization of survey data and research trends. The work concludes by synthesizing critical research gaps and outlining promising future directions. By synthesizing insights from over 100 recent research articles and providing a critical analysis of evaluation methodologies, this survey offers an essential roadmap for researchers and practitioners developing scalable, efficient, and privacy-aware distributed intelligence for the 6G and AI era.

With the growing demand for multimedia services, Dynamic Adaptive Streaming over HTTP (DASH) has become a key solution for delivering high-quality video content. In this work, we consider an Edge-DASH scenario and formulate a joint optimization problem that involves four critical aspects: bitrate allocation, user-to-server assignment, caching, and bandwidth allocation. Due to the complexity of the joint problem, we decompose it into sub-problems and address them separately. To solve the resulting sub-problems, we employ deep reinforcement learning, specifically the Deep Deterministic Policy Gradient (DDPG) method, for three of them, and develop a heuristic solution for the fourth. Simulation results demonstrate that our approach enhances performance across multiple metrics, including improved video delivery, reduced buffer underflow and overflow, and more efficient caching, which collectively enable greater utilization of edge resources for streaming. Moreover, we evaluated inference latency across edge and cloud hardware, confirming sub- to few-millisecond performance suitable for real-time deployment. This showcases the benefits of combining learning-based and heuristic techniques to meet the growing demand for adaptive video streaming in edge computing environments.

Dynamic Adaptive Streaming over HTTP (DASH) is a promising solution to enhance the Quality of Experience (QoE) of mobile video services. In this paper, we consider an Edge-DASH scenario where two problems of Bitrate Allocation (BrA) and user-to-server allocation (USA) have been jointly formulated. Then, we exploit Deep Reinforcement Learning (DRL) algorithm to solve the USA problem and select the streaming point for users, which can be streaming from the Edge, Macro layer or cloud, and deliver the users the most appropriate bitrate respecting the QoE by solving the BrA problem. In the simulation results, we have demonstrated that our Deep Deterministic Policy Gradient (DDPG) outperforms the traditional solution in terms of bitrate allocation.