Search Results for author:
Found 21 papers, 9 papers with code
KnowledgeSG: Privacy-Preserving Synthetic Text Generation with Knowledge Distillation from Server
The success of large language models (LLMs) facilitate many parties to fine-tune LLMs on their own private data.
Leveraging Unstructured Text Data for Federated Instruction Tuning of Large Language Models
Federated instruction tuning enables multiple clients to collaboratively fine-tune a shared large language model (LLM) that can follow humans' instructions without directly sharing raw data.
Emerging Safety Attack and Defense in Federated Instruction Tuning of Large Language Models
Federated learning (FL) enables multiple parties to collaboratively fine-tune an large language model (LLM) without the need of direct data sharing.
FedLLM-Bench: Realistic Benchmarks for Federated Learning of Large Language Models
Addressing this, we propose FedLLM-Bench, which involves 8 training methods, 4 training datasets, and 6 evaluation metrics, to offer a comprehensive testbed for the FedLLM community.
AMSNet: Netlist Dataset for AMS Circuits
Today's analog/mixed-signal (AMS) integrated circuit (IC) designs demand substantial manual intervention.
Decentralized and Lifelong-Adaptive Multi-Agent Collaborative Learning
In this paper, we propose DeLAMA, a decentralized multi-agent lifelong collaborative learning algorithm with dynamic collaboration graphs.
OpenFedLLM: Training Large Language Models on Decentralized Private Data via Federated Learning
Trained on massive publicly available data, large language models (LLMs) have demonstrated tremendous success across various fields.
Self-Alignment of Large Language Models via Monopolylogue-based Social Scene Simulation
Drawing from the sociological insight that acknowledging all parties' concerns is a key factor in shaping human values, this paper proposes a novel direction to align LLMs by themselves: social scene simulation.
FedRSU: Federated Learning for Scene Flow Estimation on Roadside Units
In this paper, we introduce FedRSU, an innovative federated learning framework for self-supervised scene flow estimation.
Learn What You Need in Personalized Federated Learning
Personalized federated learning aims to address data heterogeneity across local clients in federated learning.
ConfusionPrompt: Practical Private Inference for Online Large Language Models
In response, we present ConfusionPrompt, a novel private LLM inference framework designed to obfuscate the server by: (i) decomposing the prompt into sub-prompts, and (ii) generating pseudo prompts along with the genuine sub-prompts as input to the online LLM.
Fake It Till Make It: Federated Learning with Consensus-Oriented Generation
FedCOG consists of two key components at the client side: complementary data generation, which generates data extracted from the shared global model to complement the original dataset, and knowledge-distillation-based model training, which distills knowledge from global model to local model based on the generated data to mitigate over-fitting the original heterogeneous dataset.
Federated Learning Empowered by Generative Content
In this paper, we propose a novel FL framework termed FedGC, designed to mitigate data heterogeneity issues by diversifying private data with generative content.
FedDisco: Federated Learning with Discrepancy-Aware Collaboration
However, based on our empirical observations and theoretical analysis, we find that the dataset size is not optimal and the discrepancy between local and global category distributions could be a beneficial and complementary indicator for determining aggregation weights.
GPA-Net:No-Reference Point Cloud Quality Assessment with Multi-task Graph Convolutional Network
To extract effective features for PCQA, we propose a new graph convolution kernel, i. e., GPAConv, which attentively captures the perturbation of structure and texture.
FedFM: Anchor-based Feature Matching for Data Heterogeneity in Federated Learning
This method attempts to mitigate the negative effects of data heterogeneity in FL by aligning each client's feature space.
MSTCGAN: Multiscale time conditional generative adversarial network for long-term satellite image sequence prediction
To address the sequential error accumulation issue, MSTCGAN adopts a parallel prediction framework to produce the future image sequences by a one-hot time condition input.
MetaDT: Meta Decision Tree with Class Hierarchy for Interpretable Few-Shot Learning
Then, resorting to the prior, we split each few-shot task to a set of subtasks with different concept levels and then perform class prediction via a model of decision tree.
SGMNet: Scene Graph Matching Network for Few-Shot Remote Sensing Scene Classification
In this framework, a scene graph construction module is carefully designed to represent each test remote sensing image or each scene class as a scene graph, where the nodes reflect these co-occurrence objects meanwhile the edges capture the spatial correlations between these co-occurrence objects.
RAP-Net: Region Attention Predictive Network for Precipitation Nowcasting
Natural disasters caused by heavy rainfall often cost huge loss of life and property.
MetaNODE: Prototype Optimization as a Neural ODE for Few-Shot Learning
Although the existing meta-optimizers can also be adapted to our framework, they all overlook a crucial gradient bias issue, \emph{i. e.}, the mean-based gradient estimation is also biased on sparse data.
