Advancements in Federated Learning and Fairness

The field of federated learning is moving towards addressing the challenges of data heterogeneity, concept drift, and fairness. Researchers are proposing innovative solutions such as federated incomplete multi-view clustering, knowledge distillation, and dynamic client clustering to improve the performance and robustness of federated learning models. Additionally, there is a growing focus on fairness and fairness evaluation in federated learning, with the development of libraries and benchmarks to support more robust and reproducible research. Notable papers in this area include Federated Incomplete Multi-view Clustering with Globally Fused Graph Guidance, which proposes a novel method for federated multi-view clustering, and FeDa4Fair, which introduces a library for generating tabular datasets to evaluate fair FL methods under heterogeneous client bias. Other noteworthy papers include FedDAA, which proposes a dynamic clustered FL framework to adapt to multi-source concept drift, and HybridQ, which introduces a hybrid classical-quantum generative adversarial network for skin disease image generation.

Sources

Federated Incomplete Multi-view Clustering with Globally Fused Graph Guidance

Alternates, Assemble! Selecting Optimal Alternates for Citizens' Assemblies

Heterogeneous Federated Reinforcement Learning Using Wasserstein Barycenters

Fair Contracts in Principal-Agent Games with Heterogeneous Types

PNCS:Power-Norm Cosine Similarity for Diverse Client Selection in Federated Learning

Incentivizing High-quality Participation From Federated Learning Agents

FedFitTech: A Baseline in Federated Learning for Fitness Tracking

Client Selection Strategies for Federated Semantic Communications in Heterogeneous IoT Networks

New Insights on Unfolding and Fine-tuning Quantum Federated Learning

FedBKD: Distilled Federated Learning to Embrace Gerneralization and Personalization on Non-IID Data

Distilling A Universal Expert from Clustered Federated Learning

Client Clustering Meets Knowledge Sharing: Enhancing Privacy and Robustness in Personalized Peer-to-Peer Learning

Tackling Data Heterogeneity in Federated Learning through Knowledge Distillation with Inequitable Aggregation

Collaborative Batch Size Optimization for Federated Learning

Efficient Federated Learning with Encrypted Data Sharing for Data-Heterogeneous Edge Devices

Progressive Size-Adaptive Federated Learning: A Comprehensive Framework for Heterogeneous Multi-Modal Data Systems

FedSC: Federated Learning with Semantic-Aware Collaboration

HybridQ: Hybrid Classical-Quantum Generative Adversarial Network for Skin Disease Image Generation

An Information-Theoretic Analysis for Federated Learning under Concept Drift

FedDAA: Dynamic Client Clustering for Concept Drift Adaptation in Federated Learning

FeDa4Fair: Client-Level Federated Datasets for Fairness Evaluation

Artificial Delegates Resolve Fairness Issues in Perpetual Voting with Partial Turnout

Built with on top of