The proposed mechanism is effective for reliable and fair federated learning for mobile edge computing, maintaining high accuracy under attacks from malicious ...
A reliable and fair federated learning mechanism for mobile edge computing ... Multi-task Federated Learning based on Client Scheduling in Mobile Edge Computing.
Federated learning (FL) enables data owners to collaboratively train a machine learning model without revealing their private data and sharing the global ...
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【Abstract】Federated learning-enabled mobile edge computing implements privacy-preserving collaborative machine learning of complex models.
The Federated Learning (FL) concept has recently emerged as a promising solution for mitigating the problems of unwanted bandwidth loss, data privacy, and ...
Nov 9, 2023 · This paper first proposes a fairness-based asynchronous federated learning mechanism, which reduces the adverse effects of device and data heterogeneity on the ...
FL can serve as an enabling technology in mobile edge networks since it enables the collaborative training of an ML model and also enables DL for mobile edge ...
Apr 29, 2024 · Federated learning, as a new paradigm for training statistical models in distributed edge networks, alleviates integration and training problems.
Federated learning is well suited for edge computing applications and can leverage the the computation power of edge servers and the data collected on widely ...
Nov 18, 2024 · This paper proposes a Cost-Aware FL framework with client incentive and model compression (CAFL), aiming to incentivize clients to participate in FL training ...
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