FedQV: Leveraging Quadratic Voting in Federated Learning (2024)

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Index Terms

1. FedQV: Leveraging Quadratic Voting in Federated Learning

   1. Computing methodologies

      1. Artificial intelligence

      2. Distributed computing methodologies

      3. Machine learning

   2. Security and privacy

      1. Network security

   3. Theory of computation

      1. Design and analysis of algorithms

      2. Theory and algorithms for application domains

• FedQV: Leveraging Quadratic Voting in Federated Learning

  POMACS

  Federated Learning (FL) permits different parties to collaboratively train a global model without disclosing their respective local labels. A crucial step of FL, that of aggregating local models to produce the global one, shares many similarities with ...

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• FedQV: Leveraging Quadratic Voting in Federated Learning

  SIGMETRICS/PERFORMANCE '24: Abstracts of the 2024 ACM SIGMETRICS/IFIP PERFORMANCE Joint International Conference on Measurement and Modeling of Computer Systems

  Federated Learning (FL) permits different parties to collaboratively train a global model without disclosing their respective local labels. A crucial step of FL, that of aggregating local models to produce the global one, shares many similarities with ...

  Read More

• Survey on federated learning threats: Concepts, taxonomy on attacks and defences, experimental study and challenges

  Abstract

  Federated learning is a machine learning paradigm that emerges as a solution to the privacy-preservation demands in artificial intelligence. As machine learning, federated learning is threatened by adversarial attacks against the ...

  Highlights

  • We claim that adversarial attacks are a significant challenge in federated learning.

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