This letter proposes Adaptive Split LoRA (AdaSplitLoRA), a framework that jointly optimizes adaptive low rank adaptor (LoRA) rank allocation and dynamic bandwidth allocation for communication-efficient split federated learning (SFL)-based large language model (LLM) fine-tuning. We formulate a joint per-round latency minimization problem over server-side LoRA ranks and uplink bandwidth, and decompose it into two independent subproblems. For server-side rank adaptation, we employ a gradient-based importance heuristic to address the inherent intractability of the discrete rank optimization. For uplink bandwidth allocation, we design a min–max straggler latency problem, prove its convexity, and obtain the global optimum efficiently using interior-point methods. Experimental results show that AdaSplitLoRA outperforms or remains competitive with fixed/adaptive-rank baselines while using fewer server-side adapter parameters and achieving favorable accuracy–latency tradeoffs in heterogeneous wireless SFL settings.
Wireless multimodal federated learning (MFL) is promising for privacy-preserving edge intelligence, but its practical deployment is challenged by modality heterogeneity, client resource heterogeneity, and the high communication and computation cost of multimodal models. This paper proposes a beamforming-aided wireless...
Chun-Feng Xie, Zhi-Xiong Chen, Wenqiang Yi et al.· 2026 IEEE/CIC International...· 0 citations
Federated learning (FL) enables multiple devices to collaboratively train a global model without sharing local data. However, due to limited local computing capability and communication bandwidth, FL suffers from high learning latency, especially when the model size is large. To address these issues, we propose APQ-FL,...
Xiao-Dong Li, Yulong Gao, C. Chiasserini et al.· IEEE Transactions on Cogniti...· 0 citations
Nowadays, split federated learning (SFL) has emerged as an effective paradigm for enabling privacy-preserving collaborative intelligence across heterogeneous devices with limited computation. However, SFL incurs significant communication overhead in wireless networks due to the uplink transmission of high-dimensional s...
TRISHUL is proposed, a spectral-control framework for robust federated PEFT that follows the FL no-raw-data-sharing setting but does not itself provide formal privacy guarantees, and improves convergence, stability, and final performance over federated LoRA baselines.
Experimental results demonstrate that the proposed framework improves convergence, accuracy, scalability, and signal-to-noise ratio, data rate, while simultaneously reducing latency, and energy consumption for both CIFAR-10 and FEMNIST datasets compared with the FedProx, FedADMM, LyFeD and FL-MEC benchmark schemes.
Kofi Kwarteng Abrokwa, Qi Jiang, Zhou-Qin Ma et al.· IEEE Transactions on Green C...· 0 citations
This work establishes an efficient optimization framework for SFL under resource-constrained networks that jointly optimizes model splitting and resource allocation to minimize training cost, which is defined as the weighted sum of latency and energy costs.
Wei Wei, Xian-Hao Chen· 0 citations
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