Ran Yan

AAAI Conference 2026 Conference Paper

Oblivionis: A Lightweight Learning and Unlearning Framework for Federated Large Language Models

• Fuyao Zhang
• Xinyu Yan
• Tiantong Wu
• Wenjie Li
• Tianxiang Chen
• Yang Cao
• Ran Yan
• Longtao Huang

Large Language Models (LLMs) increasingly leverage Federated Learning (FL) to utilize private, task-specific datasets for fine-tuning while preserving data privacy. However, while federated LLM frameworks effectively enable collaborative training without raw data sharing, they critically lack built-in mechanisms for regulatory compliance like GDPR’s right to be forgotten. Integrating private data heightens concerns over data quality and long-term governance, yet existing distributed training frameworks offer no principled way to selectively remove specific client contributions post-training. Due to distributed data silos, stringent privacy constraints, and the intricacies of interdependent model aggregation, federated LLM unlearning is significantly more complex than centralized LLM unlearning. To address this gap, we introduce Oblivionis, a lightweight learning and unlearning framework that enables clients to selectively remove specific private data during federated LLM training, enhancing trustworthiness and regulatory compliance. By unifying FL and unlearning as a dual optimization objective, we incorporate 6 FL and 5 unlearning algorithms for comprehensive evaluation and comparative analysis, establishing a robust pipeline for federated LLM unlearning. Extensive experiments demonstrate that Oblivionis outperforms local training, achieving a robust balance between forgetting efficacy and model utility, with cross-algorithm comparisons providing clear directions for future LLM development.

ICLR Conference 2025 Conference Paper

HexGen-2: Disaggregated Generative Inference of LLMs in Heterogeneous Environment

• Youhe Jiang
• Ran Yan
• Binhang Yuan

Disaggregating the prefill and decoding phases represents an effective new paradigm for generative inference of large language models (LLM). This approach offers some significant system advantages, such as eliminating prefill-decoding interference and optimizing resource allocation. However, it is still an challenging open problem about how to deploy the disaggregated inference paradigm across a group of heterogeneous GPUs, which can be an economic alternative of the deployment over the homogeneous high performance GPUs. Towards this end, we introduce HexGen-2, a distributed system for high throughput and cost-efficient LLM serving on heterogeneous GPUs following the disaggragated paradigm. Built on top of HexGen, the core component of HexGen-2 is a sophisticated scheduling algorithm that formalizes the allocation of disaggregated LLM inference computations and communications over heterogeneous GPUs and network connections as a constraint optimization problem. We leverage the graph partitioning and max-flow algorithm to co-optimize resource allocation, parallel strategies for distinct inference phases, and the efficiency of inter-phase key-value (KV) cache communications. We conduct extensive experiments to evaluate HexGen-2, i.e., on OPT (30B) and Llama-2 (70B) models in various real-world settings, the results reveal that HexGen-2 delivers up to a 2.0$\times$ and on average a 1.3$\times$ improvement in serving throughput, reduces the average inference latency by 1.5$\times$ compared with state-of-the-art systems given the same price budget, and achieves comparable inference performance with a 30% lower price budget.

NeurIPS Conference 2025 Conference Paper

MeCeFO: Enhancing LLM Training Robustness via Fault-Tolerant Optimization

• Rizhen Hu
• Yutong He
• Ran Yan
• Mou Sun
• Binhang Yuan
• Kun Yuan

As distributed optimization scales to meet the demands of Large Language Model (LLM) training, hardware failures become increasingly non-negligible. Existing fault-tolerant training methods often introduce significant computational or memory overhead, demanding additional resources. To address this challenge, we propose **Me**mory- and **C**omputation- **e**fficient **F**ault-tolerant **O**ptimization (**MeCeFO**), a novel algorithm that ensures robust training with minimal overhead. When a computing node fails, MeCeFO seamlessly transfers its training task to a neighboring node while employing memory- and computation-efficient algorithmic optimizations to minimize the extra workload imposed on the neighboring node handling both tasks. MeCeFO leverages three key algorithmic designs: (i) Skip-connection, which drops the multi-head attention (MHA) module during backpropagation for memory- and computation-efficient approximation; (ii) Recomputation, which reduces activation memory in feedforward networks (FFNs); and (iii) Low-rank gradient approximation, enabling efficient estimation of FFN weight matrix gradients. Theoretically, MeCeFO matches the convergence rate of conventional distributed training, with a rate of $\mathcal{O}(1/\sqrt{nT})$, where $n$ is the data parallelism size and $T$ is the number of iterations. Empirically, MeCeFO maintains robust performance under high failure rates, incurring only a 4. 18\% drop in throughput, demonstrating $5. 0\times$ to $6. 7\times$ greater resilience than previous SOTA approaches.

EAAI Journal 2025 Journal Article

Ship fuel consumption prediction based on transfer learning: Models and applications

• Xi Luo
• Mingyang Zhang
• Yi Han
• Ran Yan
• Shuaian Wang

EAAI Journal 2024 Journal Article

A data mining-then-predict method for proactive maritime traffic management by machine learning

• Zhao Liu
• Wanli Chen
• Cong Liu
• Ran Yan
• Mingyang Zhang

ICRA Conference 2024 Conference Paper

Globalizing Local Features: Image Retrieval Using Shared Local Features with Pose Estimation for Faster Visual Localization

• Wenzheng Song
• Ran Yan
• Boshu Lei
• Takayuki Okatani

Visual localization is an important sub-task in SfM and visual SLAM that involves estimating a 6-DoF camera pose for an input query image relative to a given 3D model of the environment. The most accurate approach is a hierarchical one that splits the task into two stages: image retrieval and camera pose estimation. Each stage requires different image features, with global features compactly encoding holistic image information for the first stage and local features encoding the appearance around salient image points for the second stage. While existing methods use independent networks to extract these features, one for global and one for local, this strategy is suboptimal in terms of computational efficiency. In this paper, we propose a novel approach that achieves state-of-the-art inference accuracy with significantly improved efficiency. Our approach’s core component is SuperGF, a network that aggregates local features optimized for camera pose estimation to create a global feature that enables precise image retrieval. Through extensive experiments on the standard benchmark tests, we demonstrate that the method offers a better trade-off between accuracy and computational cost.

ICML Conference 2024 Conference Paper

HexGen: Generative Inference of Large Language Model over Heterogeneous Environment

• Youhe Jiang
• Ran Yan
• Xiaozhe Yao
• Yang Zhou
• Beidi Chen
• Binhang Yuan

Serving generative inference of the large language model is a crucial component of contemporary AI applications. In this paper, our focus lies in deploying such services in a heterogeneous and cross-datacenter setting to mitigate the substantial inference costs typically associated with a single centralized datacenter. Towards this end, we propose HexGen, a flexible distributed inference engine that uniquely supports the asymmetric partition of generative inference computations over both tensor model parallelism and pipeline parallelism, which allows for effective deployment across diverse GPUs interconnected by a fully heterogeneous network. We further propose a sophisticated scheduling algorithm grounded in constrained optimization that can adaptively assign asymmetric inference computation across the GPUs to fulfill inference requests while maintaining acceptable latency levels. We conduct an extensive empirical study to evaluate the efficiency of HexGen by serving the state-of-the-art Llama-2 (70B) model. The experimental results suggest that HexGen can choose to achieve up to $2. 3\times$ lower latency deadlines or tolerate up to $4\times$ more traffic request rates compared with the homogeneous baseline given the same budget.

NeurIPS Conference 2024 Conference Paper

RFLPA: A Robust Federated Learning Framework against Poisoning Attacks with Secure Aggregation

• Peihua Mai
• Ran Yan
• Yan Pang

Federated learning (FL) allows multiple devices to train a model collaboratively without sharing their data. Despite its benefits, FL is vulnerable to privacy leakage and poisoning attacks. To address the privacy concern, secure aggregation (SecAgg) is often used to obtain the aggregation of gradients on sever without inspecting individual user updates. Unfortunately, existing defense strategies against poisoning attacks rely on the analysis of local updates in plaintext, making them incompatible with SecAgg. To reconcile the conflicts, we propose a robust federated learning framework against poisoning attacks (RFLPA) based on SecAgg protocol. Our framework computes the cosine similarity between local updates and server updates to conduct robust aggregation. Furthermore, we leverage verifiable packed Shamir secret sharing to achieve reduced communication cost of $O(M+N)$ per user, and design a novel dot product aggregation algorithm to resolve the issue of increased information leakage. Our experimental results show that RFLPA significantly reduces communication and computation overhead by over $75\%$ compared to the state-of-the-art secret sharing method, BREA, while maintaining competitive accuracy.

ICML Conference 2024 Conference Paper

Split-and-Denoise: Protect large language model inference with local differential privacy

• Peihua Mai
• Ran Yan
• Zhe Huang
• Youjia Yang
• Yan Pang

Large Language Models (LLMs) excel in natural language understanding by capturing hidden semantics in vector space. This process enriches the value of text embeddings for various downstream tasks, thereby fostering the Embedding-as-a-Service (EaaS) business model. However, the risk of privacy leakage due to direct text transmission to servers remains a critical concern. To address this, we introduce Split-N-Denoise (SnD), an private inference framework that splits the model to execute the token embedding layer on the client side at minimal computational cost. This allows the client to introduce noise prior to transmitting the embeddings to the server, and subsequently receive and denoise the perturbed output embeddings for downstream tasks. Our approach is designed for the inference stage of LLMs and requires no modifications to the model parameters. Extensive experiments demonstrate SnD’s effectiveness in optimizing the privacy-utility tradeoff across various LLM architectures and diverse downstream tasks. The results reveal an improvement in performance under the same privacy budget compared to the baselines by over 10% on average, offering clients a privacy-preserving solution for local privacy protection.