Federated Learning Advancements

The field of federated learning is moving towards more efficient and robust methods for decentralized model training. Researchers are developing innovative techniques to address the challenges of non-independent and identically distributed (non-IID) data, communication overhead, and privacy concerns. Notably, there is a growing focus on federated learning frameworks that can handle heterogeneous data and adaptive dropout, as well as methods for domain adaptation and few-shot learning. Additionally, the development of simulation frameworks and tools for federated learning is facilitating the evaluation and comparison of different approaches. Overall, these advancements are enabling more effective and efficient federated learning solutions for a wide range of applications. Noteworthy papers include:

  • MTF-Grasp, which proposes a multi-tier federated learning approach for robotic grasping that outperforms conventional FL setups.
  • Domain Borders Are There to Be Crossed With Federated Few-Shot Adaptation, which introduces a federated learning framework for real-world client adaptation in industrial settings that achieves competitive adaptation on low-end client devices with limited target samples.

Sources

Lightweight Federated Learning over Wireless Edge Networks

Networked Information Aggregation via Machine Learning

MTF-Grasp: A Multi-tier Federated Learning Approach for Robotic Grasping

Domain Borders Are There to Be Crossed With Federated Few-Shot Adaptation

Convergence of Agnostic Federated Averaging

Efficient Federated Learning with Heterogeneous Data and Adaptive Dropout

FLsim: A Modular and Library-Agnostic Simulation Framework for Federated Learning

D3FL: Data Distribution and Detrending for Robust Federated Learning in Non-linear Time-series Data

Granular feedback merits sophisticated aggregation

A Distributed Generative AI Approach for Heterogeneous Multi-Domain Environments under Data Sharing constraints

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