Federated learning (FL) is a popular distributed learning framework where multiple clients perform local training and a server aggregates the locally updated models. FL enables decentralized training while preserving the privacy of clients'datasets. However, non-independent and identically distributed (non-IID) or noisy datasets can lead to low model accuracy or high convergence latency. Precluding these clients through client selection may mitigate the problem, but heavily biased client selections may also degrade the learning performance. In this study, we first experimentally measure the impact of non-IID data (including skews in data quantity and label distribution), noisy data, and fairness in client selection on model accuracy and convergence. We then propose a privacy-preserving scoring method to assess each client's contribution in FL, with experiments conducted to demonstrate the effectiveness of the proposed assessment.
Open Radio Access Network (O-RAN) disaggregates the traditional base station into the Radio Unit (RU), Distributed Unit (DU), and Central Unit (CU) with standardized open interfaces, enabling multi-vendor interoperability and reducing deployment costs. However, realizing per-flow network slicing at the DU while simultaneously meeting the high-throughput demands of 5G remains a significant challenge. This paper presents DPDK-DU-NS, a DPDK-enabled O-RAN DU that supports per-flow network slicing and bandwidth management in compliance with 3GPP 5G QoS flow and bearer management specifications. DPDK-DU-NS separates the control plane and user plane of an OpenAirInterface (OAI)-based DU and offloads user plane functions to Intel DPDK to achieve high-speed packet processing. We further propose Adaptive Metering, a dynamic bandwidth allocation mechanism that provides Guaranteed Bit Rate (GBR) service while fairly distributing residual capacity among active QoS flows, leveraging the Two-Rate Three-Color Marker (trTCM) algorithm. Experimental results demonstrate that the system enforces 3GPP-compliant per-flow bandwidth guarantees and limits with near-perfect fairness, ensuring robust network slice isolation. These findings, coupled with the achieved 10 Gbps line-rate throughput, validate the DPDK-DU-NS user plane as a high-performance and scalable foundation for next-generation O-RAN DU implementations.
