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.
Advancements in Federated Learning and Fairness
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