Optimizing Federated Learning for Heterogeneity and Efficiency

The field of federated learning (FL) is witnessing significant advancements aimed at addressing challenges such as data heterogeneity, communication efficiency, and model personalization across diverse environments, including edge computing and IoT applications. Recent developments emphasize innovative frameworks that optimize model training and aggregation processes to enhance performance while reducing computational and communication overhead. Key innovations include the introduction of one-shot federated learning, which minimizes communication rounds without compromising model accuracy, and the use of synthetic data distillation to mitigate data heterogeneity issues. Additionally, hierarchical and split federated learning approaches are being refined to better suit multi-tier IoT environments, ensuring both efficiency and accuracy. Personalized FL methods are also advancing, with adaptive layer-wise learning and learnable sparse customization techniques showing promise in tackling non-IID data and system heterogeneity. Theoretical analyses and empirical evaluations consistently demonstrate the superiority of these new methods over traditional FL paradigms, paving the way for more practical and scalable implementations in various domains.

Noteworthy papers include: 'One Communication Round is All It Needs for Federated Fine-Tuning Foundation Models,' which reveals the potential of one-shot federated fine-tuning for large foundation models, and 'H-FedSN: Personalized Sparse Networks for Efficient and Accurate Hierarchical Federated Learning for IoT Applications,' which introduces a novel approach to reduce communication overhead in hierarchical FL for IoT.

Sources

FedDW: Distilling Weights through Consistency Optimization in Heterogeneous Federated Learning

One Communication Round is All It Needs for Federated Fine-Tuning Foundation Models

One-shot Federated Learning via Synthetic Distiller-Distillate Communication

DapperFL: Domain Adaptive Federated Learning with Model Fusion Pruning for Edge Devices

H-FedSN: Personalized Sparse Networks for Efficient and Accurate Hierarchical Federated Learning for IoT Applications

Federated Split Learning with Model Pruning and Gradient Quantization in Wireless Networks

Sequential Compression Layers for Efficient Federated Learning in Foundational Models

Optimizing Personalized Federated Learning through Adaptive Layer-Wise Learning

Unlocking TriLevel Learning with Level-Wise Zeroth Order Constraints: Distributed Algorithms and Provable Non-Asymptotic Convergence

Hierarchical Split Federated Learning: Convergence Analysis and System Optimization

Learnable Sparse Customization in Heterogeneous Edge Computing

Federated Foundation Models on Heterogeneous Time Series

Built with on top of