Non-iid data in federated learning: A systematic review with taxonomy, metrics, methods, frameworks and future directions
Recent advances in machine learning have highlighted Federated Learning (FL) as a
promising approach that enables multiple distributed users (so-called clients) to collectively …
promising approach that enables multiple distributed users (so-called clients) to collectively …
Global and local prompts cooperation via optimal transport for federated learning
Prompt learning in pretrained visual-language models has shown remarkable flexibility
across various downstream tasks. Leveraging its inherent lightweight nature recent research …
across various downstream tasks. Leveraging its inherent lightweight nature recent research …
FedAWR: An interactive federated active learning framework for air writing recognition
The rapid development of technology such as virtual reality and augmented reality, coupled
with the reduced direct contact due to the COVID-19 pandemic, has led to the emergence of …
with the reduced direct contact due to the COVID-19 pandemic, has led to the emergence of …
Understanding convergence and generalization in federated learning through feature learning theory
Federated Learning (FL) has attracted significant attention as an efficient privacy-preserving
approach to distributed learning across multiple clients. Despite extensive empirical …
approach to distributed learning across multiple clients. Despite extensive empirical …
Think Twice Before Selection: Federated Evidential Active Learning for Medical Image Analysis with Domain Shifts
Federated learning facilitates the collaborative learning of a global model across multiple
distributed medical institutions without centralizing data. Nevertheless the expensive cost of …
distributed medical institutions without centralizing data. Nevertheless the expensive cost of …
DP-DyLoRA: Fine-Tuning Transformer-Based Models On-Device under Differentially Private Federated Learning using Dynamic Low-Rank Adaptation
Federated learning (FL) allows clients in an Internet of Things (IoT) system to collaboratively
train a global model without sharing their local data with a server. However, clients' …
train a global model without sharing their local data with a server. However, clients' …
Federated Learning from Vision-Language Foundation Models: Theoretical Analysis and Method
Integrating pretrained vision-language foundation models like CLIP into federated learning
has attracted significant attention for enhancing generalization across diverse tasks …
has attracted significant attention for enhancing generalization across diverse tasks …
AffectFAL: Federated Active Affective Computing with Non-IID Data
Federated affective computing, which deploys traditional affective computing in a distributed
framework, achieves a trade-off between privacy and utility, and offers a wide variety of …
framework, achieves a trade-off between privacy and utility, and offers a wide variety of …
Adafl: Adaptive client selection and dynamic contribution evaluation for efficient federated learning
Federated learning is a collaborative machine learning framework where multiple clients
jointly train a global model. To mitigate communication overhead, it is common to select a …
jointly train a global model. To mitigate communication overhead, it is common to select a …
Two-Stage Active Learning for Efficient Temporal Action Segmentation
Y Su, E Elhamifar - European Conference on Computer Vision, 2024 - Springer
Training a temporal action segmentation (TAS) model on long and untrimmed videos
requires gathering framewise video annotations, which is very costly. We propose a two …
requires gathering framewise video annotations, which is very costly. We propose a two …