# Edge Cloud Computing and Federated-Split Learning in Internet of Things
###### Abstract
In [3], [PERSON] and co-authors analyze FL processes tailored for 6G standards, implementing a practical FL platform that employs Raspberry Pi devices and virtual machines as client nodes and hosts the FL server on a Windows PC. Their analysis delves into the impact of computational resources, data availability, and heating issues across heterogeneous device sets. The limited bandwidth for data transmission in edge-based IoT makes communication efficiency a critical aspect of deploying FL and SL in this domain. Optimal client selection and model aggregation offer promising approaches to achieving communication-efficient FL. In [4], the authors propose a Federated Learning via Clustering Optimization (FedCO) scheme to optimize model aggregation and reduce communication costs. In FedCo, participating clients are clustered based on the similarity of their model parameters; the best-performing client is selected from each group as a representative to communicate with the central server. The proposed FedCO approach updates clusters by repeatedly evaluating and splitting them, improving worker partitioning. The experimental results indicate that the FedCO approach is effective in reducing communication costs and improving model accuracy. Client selection and model aggregation technologies have also been leveraged in [5] for reducing model training time while improving model accuracy in FL. The authors of this paper introduce the Latency-awarE Semi-synchronous client Selection and mOdel aggregation for federated learNing (LESSON) method, which allows clients to participate at different frequencies, thus mitigating straggler issues and expediting model convergence. Their simulation results show that LESSON outperforms two baseline methods, FedAvg and FedCS, in terms of convergence speed and maintains a higher model accuracy than FedCS. Although FL is seen as a privacy-preserving distributed machine learning method, recent research has shown it to be vulnerable to some privacy attacks. Homomorphic Encryption (HE) and Differential Privacy (DP) are two promising techniques for privacy protection in FL. In [6], [PERSON] et al. first present consistent attacks on privacy in FL and then provide an overview of HE and DP techniques for securing FL in next-generation internet applications. This paper discusses the strengths and weaknesses of these techniques in different settings, as described in the literature, with a particular focus on the trade-off between privacy and convergence, as well as the computation overheads involved. Blockchain technologies have been employed as an effective approach to improving FL performance in a variety of ways, including protecting data privacy and system security. In [7], [PERSON] et al. examine the EIFFeL framework, a protocol for decentralized real-time messaging in continuous integration and delivery pipelines. The authors introduce an enhanced scheme that leverages the trustworthy nature of blockchain technology. The proposed scheme eliminates the need for a central server and any other third party, thereby mitigating the risks associated with any potential breach. Combining federated and split learning may aid in fully exploiting the advantages of both while mitigating their respective shortcomings. Thus, this matter has recently become an active research topic attracting extensive interest. In [8], the authors propose a multi-level split-federated learning (multi-level SFL) framework that merges the benefits of both SL and FL. This framework leverages the Message Queuing Telemetry Transport (MQTT) protocol to geographically cluster IoT devices, employing edge and fog computing layers for the initial model parameter aggregation. Their simulation experiments verify that the multi-level SFL framework outperforms traditional SFL by improving the model accuracy and convergence speed in large-scale IoT environments. The aforementioned research primarily focuses on addressing the challenges of deploying FL/SL frameworks in edge cloud computing-based IoT environments.