Nov 2, 2021 · We propose a taxonomy of FAFL approaches covering major steps in FL, including client selection, optimization, contribution evaluation and incentive ...
In addition, we discuss the main metrics for experimentally evaluating the performance of FAFL approaches and suggest promising future research directions.
Oct 12, 2021 · We propose a new federated learning framework called FAFL in which the goal is to minimize the worst-case weighted client losses over an uncertainty set.
A taxonomy of FAFL approaches covering major steps in FL, including client selection, optimization, contribution evaluation, and incentive distribution is ...
... To achieve fairness in federated learning, the training set must comprise diverse institutions from different geographies and socioeconomic categories.
Finally, we demonstrate the good performance of our proposal both in terms of fairness and privacy through experi- ments conducted over three real datasets.
Nov 14, 2024 · Federated Learning (FL) enables the distributed training of a model across multiple data owners under the orchestration of a central server ...
Oct 6, 2021 · In this article, we tackle this issue by designing fairness-aware and time-sensitive task allocation mechanisms in asynchronous FL for CEI.
Nov 20, 2024 · Recent years have seen the rapid development of fairness-aware machine learning in mitigating unfairness or discrimination in ...
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Nov 24, 2024 · Towards Fairness Aware Federated Learning https://ifoxprojects.com/ IEEE PROJECTS 2024-2025 TITLE LIST WhatsApp : +91-7397059998 Link: ...