Advancements in Federated Learning Efficiency and Fairness

The field of federated learning (FL) is rapidly evolving, with recent research focusing on enhancing the efficiency, fairness, and robustness of distributed machine learning models. A significant trend is the exploration of novel frameworks and algorithms that address the challenges posed by non-IID data, label skews, and data imbalances, which are prevalent in real-world applications. Innovations include the development of specialized models for accurate data labeling in hierarchical wireless networks, the introduction of gradient alignment techniques to mitigate error asymmetry, and the application of game theory to ensure fairness in distributed learning environments. Additionally, there is a growing interest in leveraging spiking neural networks for energy-efficient FL and in analyzing the resilience of FL models to adversarial attacks. These advancements are paving the way for more scalable, secure, and equitable FL systems.

Noteworthy papers include:

  • A study on the impact of cut layer selection in Split Federated Learning, revealing significant performance variations based on the chosen strategy.
  • The introduction of fluke, a Python package designed to simplify the development of new FL algorithms, highlighting its flexibility and ease of use for researchers.
  • Research presenting FedGA, a method that employs gradient alignment to mitigate error asymmetry in FL, demonstrating superior performance in model convergence and accuracy.
  • A novel framework, FedLEC, which addresses label skews in FL with Spiking Neural Networks, showing substantial accuracy improvements across various datasets.

Sources

The Impact of Cut Layer Selection in Split Federated Learning

fluke: Federated Learning Utility frameworK for Experimentation and research

Fair Distributed Machine Learning with Imbalanced Data as a Stackelberg Evolutionary Game

FedGA: Federated Learning with Gradient Alignment for Error Asymmetry Mitigation

Efficient Data Labeling and Optimal Device Scheduling in HWNs Using Clustered Federated Semi-Supervised Learning

FedLEC: Effective Federated Learning Algorithm with Spiking Neural Networks Under Label Skews

An Empirical Analysis of Federated Learning Models Subject to Label-Flipping Adversarial Attack

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