Advances in Secure and Efficient Learning for Distributed Systems

The field of distributed systems is moving towards developing more secure and efficient learning algorithms, with a focus on federated learning and Byzantine-resilient methods. Researchers are exploring new approaches to improve the robustness and accuracy of models in decentralized environments, including the use of meta-learning frameworks, lightweight gradient masking mechanisms, and clustering algorithms. Another notable direction is the development of fast and efficient algorithms for topology optimization and estimation in distribution grids, which can help achieve better load monitoring, operation, and control of power distribution systems. Noteworthy papers in this area include:

  • ATRO, which introduces a solver-free framework for topology and routing optimization in reconfigurable datacenter networks.
  • FedStrategist, which proposes a meta-learning framework for adaptive and robust aggregation in federated learning.
  • DP2Guard, which presents a lightweight and Byzantine-robust privacy-preserving federated learning scheme for industrial IoT.
  • Fast Distribution Grid Topology Estimation via Subset Sum, which proposes a novel and ultra-fast topology identification method for distribution grids.

Sources

ATRO: A Fast Solver-Free Algorithm for Topology and Routing Optimization of Reconfigurable Datacenter Networks

Byzantine-resilient federated online learning for Gaussian process regression

FedStrategist: A Meta-Learning Framework for Adaptive and Robust Aggregation in Federated Learning

FORTA: Byzantine-Resilient FL Aggregation via DFT-Guided Krum

Rec-AD: An Efficient Computation Framework for FDIA Detection Based on Tensor Train Decomposition and Deep Learning Recommendation Model

Clustered Federated Learning for Generalizable FDIA Detection in Smart Grids with Heterogeneous Data

Fast Feeder Reconfiguration via Mesh Adaptive Direct Search in Black-Box Distribution System Environments

DP2Guard: A Lightweight and Byzantine-Robust Privacy-Preserving Federated Learning Scheme for Industrial IoT

Toward a Lightweight and Robust Design for Caching with Predictions

Fast Distribution Grid Topology Estimation via Subset Sum

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