4 citations · 7 across the 8 of their papers we have counts for
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cs.LG2026
Beyond performance-wise Contribution Evaluation in Federated Learning
Balazs Pejo
Federated learning offers a privacy-friendly collaborative learning framework, yet its success, like any joint venture, hinges on the contributions of its participants. Existing cl…
cs.LG2025
On the Fragility of Contribution Score Computation in Federated Learning
Balazs Pejo, Marcell Frank, Krisztian Varga +2
This paper investigates the fragility of contribution evaluation in federated learning, a critical mechanism for ensuring fairness and incentivizing participation. We argue that co…
cs.LG2020★ 3 cited
Quality Inference in Federated Learning with Secure Aggregation
Balázs Pejó, Gergely Biczók
Federated learning algorithms are developed both for efficiency reasons and to ensure the privacy and confidentiality of personal and business data, respectively. Despite no data b…