Search Results for author:
Found 21 papers, 6 papers with code
FL-TAC: Enhanced Fine-Tuning in Federated Learning via Low-Rank, Task-Specific Adapter Clustering
Although large-scale pre-trained models hold great potential for adapting to downstream tasks through fine-tuning, the performance of such fine-tuned models is often limited by the difficulty of collecting sufficient high-quality, task-specific data.
Analyzing and Overcoming Local Optima in Complex Multi-Objective Optimization by Decomposition-Based Evolutionary Algorithms
When addressing the challenge of complex multi-objective optimization problems, particularly those with non-convex and non-uniform Pareto fronts, Decomposition-based Multi-Objective Evolutionary Algorithms (MOEADs) often converge to local optima, thereby limiting solution diversity.
Moderating Illicit Online Image Promotion for Unsafe User-Generated Content Games Using Large Vision-Language Models
Online user-generated content games (UGCGs) are increasingly popular among children and adolescents for social interaction and more creative online entertainment.
Growing from Exploration: A self-exploring framework for robots based on foundation models
In this work, we propose a framework named GExp, which enables robots to explore and learn autonomously without human intervention.
DualTeacher: Bridging Coexistence of Unlabelled Classes for Semi-supervised Incremental Object Detection
A commonly-used strategy for supervised IOD is to encourage the current model (as a student) to mimic the behavior of the old model (as a teacher), but it generally fails in SSIOD because a dominant number of object instances from old and new classes are coexisting and unlabelled, with the teacher only recognizing a fraction of them.
Inclusive Data Representation in Federated Learning: A Novel Approach Integrating Textual and Visual Prompt
Federated Learning (FL) is often impeded by communication overhead issues.
Federated PAC-Bayesian Learning on Non-IID data
Existing research has either adapted the Probably Approximately Correct (PAC) Bayesian framework for federated learning (FL) or used information-theoretic PAC-Bayesian bounds while introducing their theorems, but few considering the non-IID challenges in FL.
Cooperative Multi-Type Multi-Agent Deep Reinforcement Learning for Resource Management in Space-Air-Ground Integrated Networks
The Space-Air-Ground Integrated Network (SAGIN), integrating heterogeneous devices including low earth orbit (LEO) satellites, unmanned aerial vehicles (UAVs), and ground users (GUs), holds significant promise for advancing smart city applications.
AQUILA: Communication Efficient Federated Learning with Adaptive Quantization in Device Selection Strategy
In response, this paper introduces AQUILA (adaptive quantization in device selection strategy), a novel adaptive framework devised to effectively handle these issues, enhancing the efficiency and robustness of FL.
PerFedRec++: Enhancing Personalized Federated Recommendation with Self-Supervised Pre-Training
Each user learns a personalized model by combining the global federated model, the cluster-level federated model, and its own fine-tuned local model.
BEVFusion4D: Learning LiDAR-Camera Fusion Under Bird's-Eye-View via Cross-Modality Guidance and Temporal Aggregation
Integrating LiDAR and Camera information into Bird's-Eye-View (BEV) has become an essential topic for 3D object detection in autonomous driving.
Understanding the Robustness of 3D Object Detection with Bird's-Eye-View Representations in Autonomous Driving
However, there still lacks a systematic understanding of the robustness of these vision-dependent BEV models, which is closely related to the safety of autonomous driving systems.
Optimizing Trading Strategies in Quantitative Markets using Multi-Agent Reinforcement Learning
Quantitative markets are characterized by swift dynamics and abundant uncertainties, making the pursuit of profit-driven stock trading actions inherently challenging.
Stabilizing and Improving Federated Learning with Non-IID Data and Client Dropout
The label distribution skew induced data heterogeniety has been shown to be a significant obstacle that limits the model performance in federated learning, which is particularly developed for collaborative model training over decentralized data sources while preserving user privacy.
Visual-tactile Fusion for Transparent Object Grasping in Complex Backgrounds
Here, a visual-tactile fusion framework for transparent object grasping under complex backgrounds and variant light conditions is proposed, including the grasping position detection, tactile calibration, and visual-tactile fusion based classification.
WS-3D-Lane: Weakly Supervised 3D Lane Detection With 2D Lane Labels
To the best of our knowledge, WS-3D-Lane is the first try of 3D lane detection under weakly supervised setting.
FedHAP: Federated Hashing with Global Prototypes for Cross-silo Retrieval
To overcome these challenges, we propose a novel federated hashing framework that enables participating clients to jointly train the shared deep hashing model by leveraging the prototypical hash codes for each class.
Consistent Attack: Universal Adversarial Perturbation on Embodied Vision Navigation
Among different adversarial noises, universal adversarial perturbations (UAP), i. e., a constant image-agnostic perturbation applied on every input frame of the agent, play a critical role in Embodied Vision Navigation since they are computation-efficient and application-practical during the attack.
Deep Leakage from Model in Federated Learning
In this paper, we present two novel frameworks to demonstrate that transmitting model weights is also likely to leak private local data of clients, i. e., (DLM and DLM+), under the FL scenario.
SAFARI: Sparsity enabled Federated Learning with Limited and Unreliable Communications
It is demonstrated that SAFARI under unreliable communications is guaranteed to converge at the same rate as the standard FedAvg with perfect communications.
On the Convergence of Heterogeneous Federated Learning with Arbitrary Adaptive Online Model Pruning
In this paper, we present a unifying framework for heterogeneous FL algorithms with {\em arbitrary} adaptive online model pruning and provide a general convergence analysis.
