Advancements in Federated Learning: Personalization, Efficiency, and Privacy

The field of Federated Learning (FL) is rapidly evolving to address the challenges posed by data heterogeneity, communication efficiency, and privacy concerns. Recent developments have focused on personalizing FL to cater to non-IID data distributions across clients, enhancing communication protocols to reduce overhead, and integrating privacy-preserving mechanisms without compromising model performance. Innovations include the formulation of joint optimization problems that consider real-world constraints such as wireless channel conditions, the introduction of adaptive optimization frameworks that accommodate client heterogeneity, and the development of novel algorithms that ensure fairness and fast convergence. Furthermore, the integration of FL with multi-task learning and the exploration of vertical FL with differential privacy highlight the field's expansion into more complex and privacy-sensitive applications. These advancements collectively aim to make FL more robust, efficient, and applicable to a wider range of real-world scenarios.

Noteworthy Papers

  • pFedWN: Introduces a personalized FL framework optimized for D2D wireless networks, addressing data heterogeneity and channel variability.
  • Client-Centric Federated Adaptive Optimization: Proposes a novel FL framework that supports arbitrary client participation and asynchronous server aggregation, improving adaptability to system heterogeneity.
  • ColNet: Bridges the gap in decentralized federated multi-task learning by introducing a framework for heterogeneous tasks, enhancing performance in decentralized settings.
  • DQN-Fed: A second-order FL framework that ensures fairness and leverages the fast convergence properties of quasi-Newton methods.
  • pMixFed: Offers a dynamic, layer-wise PFL approach that integrates mixup between shared global and personalized local models, improving handling of data heterogeneity.
  • FedQVR: A communication-efficient FL algorithm that achieves heterogeneity-robustness through a sophisticated variance-reduced scheme.
  • FedBSS: Mitigates client drift in a sample-level manner, showing scalability and robustness across different settings.
  • CEPAM: Introduces a novel approach for achieving communication efficiency and privacy protection simultaneously in FL.
  • Privacy-Preserving Early Detection of Sexual Predators: Implements a privacy-preserving pipeline for detecting online grooming, balancing privacy and utility.
  • Anomaly Detection in Double-entry Bookkeeping Data: Proposes a non-model share-type FL method for anomaly detection, preserving data confidentiality and reducing communication overhead.
  • Learning-based Distributed Model Predictive Control: Combines Multi-agent Bayesian Optimization with Distributed Model Predictive Control for improved closed-loop performance.
  • Communication-Efficient Distributed Kalman Filtering: Enhances communication efficiency in distributed Kalman filtering by eliminating the need for dual variable exchange.
  • Communication-Efficient Stochastic Distributed Learning: Designs a novel algorithm based on distributed ADMM to tackle high communication costs and large datasets.
  • FedPref: Addresses preference heterogeneity in FL, facilitating personalized learning under multiple objectives.
  • Local Steps Speed Up Local GD: Shows that local updates can significantly speed up convergence in distributed logistic regression with heterogeneous data.
  • Privacy-Preserving Personalized Federated Prompt Learning: Proposes a differentially private FL approach for multimodal LLMs, balancing personalization, generalization, and privacy.
  • PBM-VFL: Introduces a vertical FL algorithm with differential privacy guarantees, analyzing feature and sample privacy.

Sources

pFedWN: A Personalized Federated Learning Framework for D2D Wireless Networks with Heterogeneous Data

Client-Centric Federated Adaptive Optimization

ColNet: Collaborative Optimization in Decentralized Federated Multi-task Learning Systems

Distributed Quasi-Newton Method for Fair and Fast Federated Learning

pMixFed: Efficient Personalized Federated Learning through Adaptive Layer-Wise Mixup

Communication-Efficient Federated Learning by Quantized Variance Reduction for Heterogeneous Wireless Edge Networks

Federated Learning with Sample-level Client Drift Mitigation

Communication-Efficient and Privacy-Adaptable Mechanism for Federated Learning

Enhancing Privacy in the Early Detection of Sexual Predators Through Federated Learning and Differential Privacy

Anomaly Detection in Double-entry Bookkeeping Data by Federated Learning System with Non-model Sharing Approach

Learning-based Distributed Model Predictive Control using Multi-Agent Bayesian Optimization

Communication-Efficient Distributed Kalman Filtering using ADMM

Communication-Efficient Stochastic Distributed Learning

FedPref: Federated Learning Across Heterogeneous Multi-objective Preferences

Local Steps Speed Up Local GD for Heterogeneous Distributed Logistic Regression

Privacy-Preserving Personalized Federated Prompt Learning for Multimodal Large Language Models

PBM-VFL: Vertical Federated Learning with Feature and Sample Privacy

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