Search Results for author:
Found 59 papers, 8 papers with code
FedCCL: Federated Dual-Clustered Feature Contrast Under Domain Heterogeneity
First, we employ clustering on the local representations of each client, aiming to capture intra-class information based on these local clusters at a high level of granularity.
Logit Calibration and Feature Contrast for Robust Federated Learning on Non-IID Data
Federated learning (FL) is a privacy-preserving distributed framework for collaborative model training on devices in edge networks.
Sora as an AGI World Model? A Complete Survey on Text-to-Video Generation
Text-to-video generation marks a significant frontier in the rapidly evolving domain of generative AI, integrating advancements in text-to-image synthesis, video captioning, and text-guided editing.
Towards Robust Federated Learning via Logits Calibration on Non-IID Data
Meanwhile, the non-independent and identically distributed (non-IID) challenge of data distribution between edge devices can further degrade the performance of models.
Cross-Modal Prototype based Multimodal Federated Learning under Severely Missing Modality
In this paper, we propose Multimodal Federated Cross Prototype Learning (MFCPL), a novel approach for MFL under severely missing modalities by conducting the complete prototypes to provide diverse modality knowledge in modality-shared level with the cross-modal regularization and modality-specific level with cross-modal contrastive mechanism.
Attention on Personalized Clinical Decision Support System: Federated Learning Approach
Thus, our objective is to provide a personalized clinical decision support system with evolvable characteristics that can deliver accurate solutions and assist healthcare professionals in medical diagnosing.
LW-FedSSL: Resource-efficient Layer-wise Federated Self-supervised Learning
Many recent studies integrate federated learning (FL) with self-supervised learning (SSL) to take advantage of raw training data distributed across edge devices.
Transformers with Attentive Federated Aggregation for Time Series Stock Forecasting
Recent innovations in transformers have shown their superior performance in natural language processing (NLP) and computer vision (CV).
OnDev-LCT: On-Device Lightweight Convolutional Transformers towards federated learning
Federated learning (FL) has emerged as a promising approach to collaboratively train machine learning models across multiple edge devices while preserving privacy.
MobileSAMv2: Faster Segment Anything to Everything
The efficiency bottleneck of SegEvery with SAM, however, lies in its mask decoder because it needs to first generate numerous masks with redundant grid-search prompts and then perform filtering to obtain the final valid masks.
Federated Learning with Diffusion Models for Privacy-Sensitive Vision Tasks
We conduct experiments on various FL scenarios, and our findings demonstrate that federated diffusion models have great potential to deliver vision services to privacy-sensitive domains.
Contrastive encoder pre-training-based clustered federated learning for heterogeneous data
Together, self-supervised pre-training and client clustering can be crucial components for tackling the data heterogeneity issues of FL.
Joint User Pairing and Beamforming Design of Multi-STAR-RISs-Aided NOMA in the Indoor Environment via Multi-Agent Reinforcement Learning
Motivated by the above, we study the joint user pairing for NOMA and beamforming design of Multi-STAR-RISs in an indoor environment.
An Efficient Federated Learning Framework for Training Semantic Communication System
Semantic communication has emerged as a pillar for the next generation of communication systems due to its capabilities in alleviating data redundancy.
Federated Deep Equilibrium Learning: A Compact Shared Representation for Edge Communication Efficiency
Federated Learning (FL) is a prominent distributed learning paradigm facilitating collaboration among nodes within an edge network to co-train a global model without centralizing data.
Causal Reasoning: Charting a Revolutionary Course for Next-Generation AI-Native Wireless Networks
We showcase how incorporating causal discovery can assist in achieving dynamic adaptability, resilience, and cognition in addressing these challenges.
MST-compression: Compressing and Accelerating Binary Neural Networks with Minimum Spanning Tree
Binary neural networks (BNNs) have been widely adopted to reduce the computational cost and memory storage on edge-computing devices by using one-bit representation for activations and weights.
FedMEKT: Distillation-based Embedding Knowledge Transfer for Multimodal Federated Learning
Bringing this concept into a system, we develop a distillation-based multimodal embedding knowledge transfer mechanism, namely FedMEKT, which allows the server and clients to exchange the joint knowledge of their learning models extracted from a small multimodal proxy dataset.
Boosting Federated Learning Convergence with Prototype Regularization
As a distributed machine learning technique, federated learning (FL) requires clients to collaboratively train a shared model with an edge server without leaking their local data.
Convergence of Communications, Control, and Machine Learning for Secure and Autonomous Vehicle Navigation
For both cases, solutions using the convergence of communication theory, control theory, and machine learning are proposed to enable effective and secure CAV navigation.
Faster Segment Anything: Towards Lightweight SAM for Mobile Applications
Concretely, we distill the knowledge from the heavy image encoder (ViT-H in the original SAM) to a lightweight image encoder, which can be automatically compatible with the mask decoder in the original SAM.
Robustness of SAM: Segment Anything Under Corruptions and Beyond
Following by interpreting the effects of synthetic corruption as style changes, we proceed to conduct a comprehensive evaluation for its robustness against 15 types of common corruption.
Understanding Segment Anything Model: SAM is Biased Towards Texture Rather than Shape
Different from label-oriented recognition tasks, the SAM is trained to predict a mask for covering the object shape based on a promt.
A Survey on Segment Anything Model (SAM): Vision Foundation Model Meets Prompt Engineering
This is an ongoing project and we intend to update the manuscript on a regular basis.
Generative AI meets 3D: A Survey on Text-to-3D in AIGC Era
AI generated content) has made remarkable progress in the past few years, among which text-guided content generation is the most practical one since it enables the interaction between human instruction and AIGC.
Segment Anything Model (SAM) Meets Glass: Mirror and Transparent Objects Cannot Be Easily Detected
Meta AI Research has recently released SAM (Segment Anything Model) which is trained on a large segmentation dataset of over 1 billion masks.
One Small Step for Generative AI, One Giant Leap for AGI: A Complete Survey on ChatGPT in AIGC Era
Overall, this work is the first to survey ChatGPT with a comprehensive review of its underlying technology, applications, and challenges.
MP-FedCL: Multiprototype Federated Contrastive Learning for Edge Intelligence
The existing single prototype-based strategy represents a class by using the mean of the feature space.
Prototype Helps Federated Learning: Towards Faster Convergence
In this paper, a prototype-based federated learning framework is proposed, which can achieve better inference performance with only a few changes to the last global iteration of the typical federated learning process.
A Complete Survey on Generative AI (AIGC): Is ChatGPT from GPT-4 to GPT-5 All You Need?
As ChatGPT goes viral, generative AI (AIGC, a. k. a AI-generated content) has made headlines everywhere because of its ability to analyze and create text, images, and beyond.
Federated Learning based Energy Demand Prediction with Clustered Aggregation
In this paper, we propose a recurrent neural network based energy demand predictor, trained with federated learning on clustered clients to take advantage of distributed data and speed up the convergence process.
Federated Learning with Intermediate Representation Regularization
Specifically, FedIntR computes a regularization term that encourages the closeness between the intermediate layer representations of the local and global models.
Neuro-symbolic Explainable Artificial Intelligence Twin for Zero-touch IoE in Wireless Network
Explainable artificial intelligence (XAI) twin systems will be a fundamental enabler of zero-touch network and service management (ZSM) for sixth-generation (6G) wireless networks.
Explainable artificial intelligence
Explainable Artificial Intelligence (XAI)
+2
CDKT-FL: Cross-Device Knowledge Transfer using Proxy Dataset in Federated Learning
Therefore, to develop robust generalized global and personalized models, conventional FL methods need redesigning the knowledge aggregation from biased local models while considering huge divergence of learning parameters due to skewed client data.
An Explainable Artificial Intelligence Framework for Quality-Aware IoE Service Delivery
First, a problem of quality-aware IoE service delivery is formulated by taking into account network dynamics and contextual metrics of IoE, where the objective is to maximize the channel quality index (CQI) of each IoE service user.
Explainable artificial intelligence
Explainable Artificial Intelligence (XAI)
+1
Risk Adversarial Learning System for Connected and Autonomous Vehicle Charging
To tackle this, we formulate an RDSS problem for the DSO, where the objective is to maximize the charging capacity utilization by satisfying the laxity risk of the DSO.
Ultra-Reliable Indoor Millimeter Wave Communications using Multiple Artificial Intelligence-Powered Intelligent Surfaces
Simulation results show that the error between policies of the optimal and the RNN-based controllers is less than 1. 5%.
Digital-Twin-Enabled 6G: Vision, Architectural Trends, and Future Directions
Therefore, enabling IoE applications over 6G requires a new framework that can be used to manage, operate, and optimize the 6G wireless system and its underlying IoE services.
Edge-computing
Networking and Internet Architecture
Edge-assisted Democratized Learning Towards Federated Analytics
Therefore, we need to design a robust learning mechanism than the FL that (i) unleashes a viable infrastructure for FA and (ii) trains learning models with better generalization capability.
Toward Multiple Federated Learning Services Resource Sharing in Mobile Edge Networks
Federated Learning is a new learning scheme for collaborative training a shared prediction model while keeping data locally on participating devices.
Federated Learning for Internet of Things: Recent Advances, Taxonomy, and Open Challenges
However, given the presence of massively distributed and private datasets, it is challenging to use classical centralized learning algorithms in the IoT.
Networking and Internet Architecture
An Incentive Mechanism for Federated Learning in Wireless Cellular network: An Auction Approach
In this paper, we consider a FL system that involves one base station (BS) and multiple mobile users.
Drive Safe: Cognitive-Behavioral Mining for Intelligent Transportation Cyber-Physical System
Further, we develop a prototype of the proposed drive safe platform to establish proof-of-concept (PoC) for the road safety in IT-CPS.
Dispersed Federated Learning: Vision, Taxonomy, and Future Directions
However, federated learning still has privacy concerns due to sensitive information inferring capability of the aggregation server using end-devices local learning models.
Distributed, Parallel, and Cluster Computing
Self-organizing Democratized Learning: Towards Large-scale Distributed Learning Systems
Inspired by Dem-AI philosophy, a novel distributed learning approach is proposed in this paper.
Delay Minimization for Federated Learning Over Wireless Communication Networks
In this paper, the problem of delay minimization for federated learning (FL) over wireless communication networks is investigated.
Federated Learning for Task and Resource Allocation in Wireless High Altitude Balloon Networks
This problem is posed as an optimization problem whose goal is to minimize the energy and time consumption for task computing and transmission by adjusting the user association, service sequence, and task allocation scheme.
Distributed and Democratized Learning: Philosophy and Research Challenges
Consequently, many emerging cross-device AI applications will require a transition from traditional centralized learning systems towards large-scale distributed AI systems that can collaboratively perform multiple complex learning tasks.
Data Freshness and Energy-Efficient UAV Navigation Optimization: A Deep Reinforcement Learning Approach
First, we formulate an energy-efficient trajectory optimization problem in which the objective is to maximize the energy efficiency by optimizing the UAV-BS trajectory policy.
Risk-Aware Energy Scheduling for Edge Computing with Microgrid: A Multi-Agent Deep Reinforcement Learning Approach
First, we formulate an optimization problem considering the conditional value-at-risk (CVaR) measurement for both energy consumption and generation, where the objective is to minimize the expected residual of scheduled energy for the MEC networks and we show this problem is an NP-hard problem.
Multi-Agent Meta-Reinforcement Learning for Self-Powered and Sustainable Edge Computing Systems
In particular, each BS plays the role of a local agent that explores a Markovian behavior for both energy consumption and generation while each BS transfers time-varying features to a meta-agent.
Reinforcement Learning Based Vehicle-cell Association Algorithm for Highly Mobile Millimeter Wave Communication
Vehicle-to-everything (V2X) communication is a growing area of communication with a variety of use cases.
Deep Learning with Persistent Homology for Orbital Angular Momentum (OAM) Decoding
To maintain lower error rate in presence of severe atmospheric turbulence, a new approach that combines effective machine learning tools from persistent homology and convolutional neural networks (CNNs) is proposed to decode the OAM modes.
Radio Resource Allocation in 5G New Radio: A Neural Networks Based Approach)
In this paper, we address the problem of RBs allocation to UEs.
Federated Learning for Edge Networks: Resource Optimization and Incentive Mechanism
IoT devices with intelligence require the use of effective machine learning paradigms.
Distributed, Parallel, and Cluster Computing
Energy Efficient Federated Learning Over Wireless Communication Networks
To solve this problem, an iterative algorithm is proposed where, at every step, closed-form solutions for time allocation, bandwidth allocation, power control, computation frequency, and learning accuracy are derived.
A Crowdsourcing Framework for On-Device Federated Learning
Federated learning (FL) rests on the notion of training a global model in a decentralized manner.
Federated Learning over Wireless Networks: Convergence Analysis and Resource Allocation
There is an increasing interest in a fast-growing machine learning technique called Federated Learning, in which the model training is distributed over mobile user equipments (UEs), exploiting UEs' local computation and training data.
On the Optimality of Reconfigurable Intelligent Surfaces (RISs): Passive Beamforming, Modulation, and Resource Allocation
In this paper, the asymptotic optimality of achievable rate in a downlink RIS system is analyzed under a practical RIS environment with its associated limitations.
Information Theory Signal Processing Information Theory
