Yuanxin Guo | Searcharxiv

arXiv2026

Modulo Quantization Coding for Primitive Relay and Diamond Channels with Correlated Noises

This paper proposes modulo quantization (MQ) coding as a simple, structured, and low-complexity scheme for channels with primitive (i.e., noiseless digital) relay links and correlated Gaussian noises across terminals. The key component of MQ coding is the modulo quantization operation, which maps a real-valued symbol to its uniform-quantization index taken modulo a fixed integer. This operation allows effective exploitation of the common noise component shared across the terminals. For the Gaussian primitive relay channel with perfectly correlated noises, where a relay has a finite-capacity link to the receiver, MQ coding can be shown to achieve the capacity of this channel. For the Gaussian primitive diamond channel with perfectly correlated noises, where two relays can forward information through finite-capacity links to a receiver that has no direct observation of the transmitted signal, MQ coding yields novel achievability bounds that improve upon previously known bounds and coincide with the cut-set upper bound in certain signal-to-noise ratio (SNR) regimes. In scenarios with highly but non-perfectly correlated noises, MQ coding can approach the performance of compress-forward (CF) at significantly lower complexity, while surpassing decode-forward (DF) for the Gaussian primitive relay channel in certain SNR ranges. For the Gaussian primitive diamond channel with non-perfectly correlated noises, MQ can outperform both CF and DF at moderate SNR.

cs.IT↗

arXiv2025

Robust Federated Finetuning of LLMs via Alternating Optimization of LoRA

Parameter-Efficient Fine-Tuning (PEFT) methods like Low-Rank Adaptation (LoRA) optimize federated training by reducing computational and communication costs. We propose RoLoRA, a federated framework using alternating optimization to fine-tune LoRA adapters. Our approach emphasizes the importance of learning up and down projection matrices to enhance expressiveness and robustness. We use both theoretical analysis and extensive experiments to demonstrate the advantages of RoLoRA over prior approaches that either generate imperfect model updates or limit expressiveness of the model. We provide a theoretical analysis on a linear model to highlight the importance of learning both the down-projection and up-projection matrices in LoRA. We validate the insights on a non-linear model and separately provide a convergence proof under general conditions. To bridge theory and practice, we conducted extensive experimental evaluations on language models including RoBERTa-Large, Llama-2-7B on diverse tasks and FL settings to demonstrate the advantages of RoLoRA over other methods.

cs.LG↗