33 citations · 147 across the 38 of their papers we have counts for
40 papers
FUSE: Fairness-aware Update Screening against Byzantine Exploitation in -Fair Federated Learning
Tiantong Sun, Jinlin Liu, Xingru Wei +4
Client-level fair federated learning, in the form of -fair federated learning (-FFL), agnostic federated learning, or tilted empirical risk minimization, has emerged as an at…
FedGRPO: Privately Optimizing Foundation Models with Group-Relative Rewards from Domain Client
Gongxi Zhu, Hanlin Gu, Lixin Fan +2
One important direction of Federated Foundation Models (FedFMs) is leveraging data from small client models to enhance the performance of a large server-side foundation model. Exis…
Large-Small Model Collaborative Framework for Federated Continual Learning
Hao Yu, Xin Yang, Boyang Fan +4
Continual learning (CL) for Foundation Models (FMs) is an essential yet underexplored challenge, especially in Federated Continual Learning (FCL), where each client learns from a p…
PPC-GPT: Federated Task-Specific Compression of Large Language Models via Pruning and Chain-of-Thought Distillation
Tao Fan, Guoqiang Ma, Yuanfeng Song +2
Compressing Large Language Models (LLMs) into task-specific Small Language Models (SLMs) encounters two significant challenges: safeguarding domain-specific knowledge privacy and m…
Handling Spatial-Temporal Data Heterogeneity for Federated Continual Learning via Tail Anchor
Hao Yu, Xin Yang, Le Zhang +4
Federated continual learning (FCL) allows each client to continually update its knowledge from task streams, enhancing the applicability of federated learning in real-world scenari…
Federated Co-tuning Framework for Large and Small Language Models
Tao Fan, Yan Kang, Guoqiang Ma +4
By adapting Large Language Models (LLMs) to domain-specific tasks or enriching them with domain-specific knowledge, we can fully harness the capabilities of LLMs. Nonetheless, a ga…
Disentangling data distribution for Federated Learning
Xinyuan Zhao, Hanlin Gu, Lixin Fan +2
Federated Learning (FL) facilitates collaborative training of a global model whose performance is boosted by private data owned by distributed clients, without compromising data pr…
Model-based Large Language Model Customization as Service
Zhaomin Wu, Jizhou Guo, Junyi Hou +3
Prominent Large Language Model (LLM) services from providers like OpenAI and Google excel at general tasks but often underperform on domain-specific applications. Current customiza…
Diffusion-Driven Data Replay: A Novel Approach to Combat Forgetting in Federated Class Continual Learning
Jinglin Liang, Jin Zhong, Hanlin Gu +6
Federated Class Continual Learning (FCCL) merges the challenges of distributed client learning with the need for seamless adaptation to new classes without forgetting old ones. The…
FedAdOb: Privacy-Preserving Federated Deep Learning with Adaptive Obfuscation
Hanlin Gu, Jiahuan Luo, Yan Kang +5
Federated learning (FL) has emerged as a collaborative approach that allows multiple clients to jointly learn a machine learning model without sharing their private data. The conce…
FedCoT: Federated Chain-of-Thought Distillation for Large Language Models
Tao Fan, Weijing Chen, Yan Kang +5
Large Language Models (LLMs) have emerged as a transformative force in artificial intelligence, demonstrating exceptional proficiency across various tasks. However, their deploymen…
FedMKT: Federated Mutual Knowledge Transfer for Large and Small Language Models
Tao Fan, Guoqiang Ma, Yan Kang +5
Recent research in federated large language models (LLMs) has primarily focused on enabling clients to fine-tune their locally deployed homogeneous LLMs collaboratively or on trans…
Federated Domain-Specific Knowledge Transfer on Large Language Models Using Synthetic Data
Haoran Li, Xinyuan Zhao, Dadi Guo +6
As large language models (LLMs) demonstrate unparalleled performance and generalization ability, LLMs are widely used and integrated into various applications. When it comes to sen…
A Survey on Contribution Evaluation in Vertical Federated Learning
Yue Cui, Chung-ju Huang, Yuzhu Zhang +4
Vertical Federated Learning (VFL) has emerged as a critical approach in machine learning to address privacy concerns associated with centralized data storage and processing. VFL fa…
No Free Lunch Theorem for Privacy-Preserving LLM Inference
Xiaojin Zhang, Yahao Pang, Yan Kang +4
Individuals and businesses have been significantly benefited by Large Language Models (LLMs) including PaLM, Gemini and ChatGPT in various ways. For example, LLMs enhance productiv…
Enhancing Security and Privacy in Federated Learning using Low-Dimensional Update Representation and Proximity-Based Defense
Wenjie Li, Kai Fan, Jingyuan Zhang +3
Federated Learning (FL) is a promising privacy-preserving machine learning paradigm that allows data owners to collaboratively train models while keeping their data localized. Desp…
Unlearning during Learning: An Efficient Federated Machine Unlearning Method
Hanlin Gu, Gongxi Zhu, Jie Zhang +4
In recent years, Federated Learning (FL) has garnered significant attention as a distributed machine learning paradigm. To facilitate the implementation of the right to be forgotte…
FedEval-LLM: Federated Evaluation of Large Language Models on Downstream Tasks with Collective Wisdom
Yuanqin He, Yan Kang, Lixin Fan +1
Federated Learning (FL) has emerged as a promising solution for collaborative training of large language models (LLMs). However, the integration of LLMs into FL introduces new chal…
Hyperparameter Optimization for SecureBoost via Constrained Multi-Objective Federated Learning
Yan Kang, Ziyao Ren, Lixin Fan +3
SecureBoost is a tree-boosting algorithm that leverages homomorphic encryption (HE) to protect data privacy in vertical federated learning. SecureBoost and its variants have been w…
FedMIA: An Effective Membership Inference Attack Exploiting "All for One" Principle in Federated Learning
Gongxi Zhu, Donghao Li, Hanlin Gu +3
Federated Learning (FL) is a promising approach for training machine learning models on decentralized data while preserving privacy. However, privacy risks, particularly Membership…
A Theoretical Analysis of Efficiency Constrained Utility-Privacy Bi-Objective Optimization in Federated Learning
Hanlin Gu, Xinyuan Zhao, Gongxi Zhu +4
Federated learning (FL) enables multiple clients to collaboratively learn a shared model without sharing their individual data. Concerns about utility, privacy, and training effici…
Grounding Foundation Models through Federated Transfer Learning: A General Framework
Yan Kang, Tao Fan, Hanlin Gu +3
Foundation Models (FMs) such as GPT-4 encoded with vast knowledge and powerful emergent abilities have achieved remarkable success in various natural language processing and comput…
FATE-LLM: A Industrial Grade Federated Learning Framework for Large Language Models
Tao Fan, Yan Kang, Guoqiang Ma +4
Large Language Models (LLMs), such as ChatGPT, LLaMA, GLM, and PaLM, have exhibited remarkable performances across various tasks in recent years. However, LLMs face two main challe…
SecureBoost Hyperparameter Tuning via Multi-Objective Federated Learning
Ziyao Ren, Yan Kang, Lixin Fan +3
SecureBoost is a tree-boosting algorithm leveraging homomorphic encryption to protect data privacy in vertical federated learning setting. It is widely used in fields such as finan…