Pedro Augusto Londe Ribeiro, Rodrigo Moreira, Larissa Ferreira Rodrigues Moreira, Joberto S. B. Martins, Tereza C. Carvalho, Flávio de Oliveira Silva
原文摘要
Federated Learning (FL) with Parameter-Efficient Fine-Tuning (PEFT) enables efficient adaptation of Large Language Models (LLMs), yet its energy and carbon costs across hardware remain poorly understood. We evaluated federated Low-Rank Adaptation (LoRA) fine-tuning on NVIDIA A40, RTX 6000 Ada, and A100 GPUs using a $2^2$ full factorial design with 135M/360M models and 10/100 local steps. CodeCarbon tracked energy and estimated CO$_2$-equivalent emissions. Descriptive allocation of variation assigned 84.8-91.2% of energy and emissions variation to local steps within these ranges. On A40 and RTX 6000 Ada, configurations with fewer steps achieved comparable accuracy with roughly 6-8-fold lower energy consumption and higher accuracy per kWh. These findings support smaller local budgets for energy-efficient federated fine-tuning within the evaluated setting.
Rodrigo Moreira, Larissa Ferreira Rodrigues Moreira, Joberto S. B. Martins, Tereza C. Carvalho, Flávio de Oliveira Silva
原文摘要
The Third Generation Partnership Project (3GPP) Technical Specification 23.501 (Release 17) defines Maximum Bit Rate (MBR) enforcement at the User Plane Function (UPF) in fifth-generation (5G) networks. Supporting differentiated service-level agreements across the radio access and core networks requires the UPF to enforce MBR limits according to configured slice priorities. During overload, the challenge is to adapt these limits while preserving that differentiation and avoiding oscillations. Building on programmable enforcement and intelligent orchestration, we propose an agentic control loop combining UPF telemetry with locally hosted Large Language Model (LLM) selection from a finite set of admissible rate candidates. Deterministic logic validates each selection and derives Packet Forwarding Control Protocol (PFCP) updates. The evaluated runs show no oscillations or fallbacks and unchanged highest-priority rate limits. These observations establish configuration preservation; delivered per-class QoS protection remains unmeasured.