Split Federated Learning and Reinforcement based Codec Switching in Edge Platform

In recent times, Split Federated Learning (SFL) is being proposed in the domain of Artificial Intelligence (AI) & Machine Learning (ML), where AI/ML models are partitioned into two or more sub-networks among clients and servers (i.e., edge/cloud). However, in the SFL approach, it is not clear on what basis the AI/ML model is partitioned among the client and the edge. In a partial offload scenario, to mitigate this issue, we propose an Optimal Split Federated Learning (O-SFL) mechanism that finds an optimal split of a DNN model based on network bandwidth where no media data is transferred but only partial output of the model is shared among clients and the edge device. However, in full offload scenario, where media data is transferred from clients to edge, it is possible that the codec that is being currently used for encoding frames is not suitable due to the current network bandwidth fluctuations. To solve this issue, we propose a Reinforcement Learning based Codec Switching (RLCS) mechanism that provides a-priori detection of a suitable codec based on current networks bandwidth conditions. We perform simulations to compare the performance of the O-SFL which provides significant improvements over SFL (considering various split points) for total training time tested with Wi-Fi and LTE network (as 5G network is not available currently). We also show the performance of RLCS mechanism with respect to the traditional fixed video codec mechanism.