No fear of classifier biases: Neural collapse inspired federated learning with synthetic and fixed classifier
Data heterogeneity is an inherent challenge that hinders the performance of federated
learning (FL). Recent studies have identified the biased classifiers of local models as the key …
learning (FL). Recent studies have identified the biased classifiers of local models as the key …
A Review of Federated Learning Methods in Heterogeneous scenarios
Federated learning emerges as a solution to the dilemma of data silos while safeguarding
data privacy, particularly relevant in the consumer electronics sector where user data privacy …
data privacy, particularly relevant in the consumer electronics sector where user data privacy …
Federated Class-Incremental Learning with Prototype Guided Transformer
Existing federated learning methods have effectively addressed decentralized learning in
scenarios involving data privacy and non-IID data. However, in real-world situations, each …
scenarios involving data privacy and non-IID data. However, in real-world situations, each …
Fedios: Decoupling orthogonal subspaces for personalization in feature-skew federated learning
Personalized federated learning (pFL) enables collaborative training among multiple clients
to enhance the capability of customized local models. In pFL, clients may have …
to enhance the capability of customized local models. In pFL, clients may have …
Physics-Driven Spectrum-Consistent Federated Learning for Palmprint Verification
Palmprint as biometrics has gained increasing attention recently due to its discriminative
ability and robustness. However, existing methods mainly improve palmprint verification …
ability and robustness. However, existing methods mainly improve palmprint verification …
Adaptive Hyper-graph Aggregation for Modality-Agnostic Federated Learning
F Qi, S Li - Proceedings of the IEEE/CVF Conference on …, 2024 - openaccess.thecvf.com
Abstract In Federated Learning (FL) the issue of statistical data heterogeneity has been a
significant challenge to the field's ongoing development. This problem is further exacerbated …
significant challenge to the field's ongoing development. This problem is further exacerbated …
FedLoGe: Joint Local and Generic Federated Learning under Long-tailed Data
Federated Long-Tailed Learning (Fed-LT), a paradigm wherein data collected from
decentralized local clients manifests a globally prevalent long-tailed distribution, has …
decentralized local clients manifests a globally prevalent long-tailed distribution, has …
Multi-level Personalized Federated Learning on Heterogeneous and Long-Tailed Data
Federated learning (FL) offers a privacy-centric distributed learning framework, enabling
model training on individual clients and central aggregation without necessitating data …
model training on individual clients and central aggregation without necessitating data …
Fedskill: Privacy preserved interpretable skill learning via imitation
Imitation learning that replicates experts' skills via their demonstrations has shown
significant success in various decision-making tasks. However, two critical challenges still …
significant success in various decision-making tasks. However, two critical challenges still …
Personalized Federated Learning Algorithm with Adaptive Clustering for Non-IID IoT Data Incorporating Multi-Task Learning and Neural Network Model …
The proliferation of IoT devices has led to an unprecedented integration of machine learning
techniques, raising concerns about data privacy. To address these concerns, federated …
techniques, raising concerns about data privacy. To address these concerns, federated …