Aug 2026· Zenodo (CERN European Organization for Nuclear Research)
Privacy-Preserving Technologies in Data
Abstract
Federated Learning (FL) is a machine learning technique that enables training models on decentralized data sources while preserving data privacy. However, traditional FL approaches often rely on global model updates, which can be vulnerable to adversarial attacks and suboptimal performance on heterogeneous data. This paper proposes a novel framework called Adaptive Parameter Adjustment Federated Learning (APAFL) that dynamically adjusts model parameters based on local data characteristics, enhancing model robustness and privacy. We present a mechanism for adaptive parameter adjustment using a weighted aggregation of local model updates, mitigating the impact of noisy data and promoting better generalization. The proposed method demonstrates significant improvements in model accuracy and privacy compared to existing FL techniques.
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