Federated Learning (FL) enables collaborative model training while preserving data privacy. However, it remains highly vulnerable to poisoning attacks, particularly coordinated label- flipping attacks. In this paper, we propose DualFed, a dual-side defense framework for detecting coordinated poisoning behaviors in FL. DualFed combines client-side adaptive behavioral anomaly detection with a server-side Collective Behavioral Correlation (CBC) mechanism. On the client side, an Adaptive Adversarially Robust Statistics (AARS) mechanism integrates trimmed estimation with Exponential Moving Average (EMA) and Exponential Moving Variance (EMV) to robustly quantify abnormal performance degradation. On the server side, CBC aggregates client anomaly reports over a sliding temporal window to detect coordinated attacks. Once an attack is confirmed, DualFed activates a non-punitive self-recovery mechanism. Under non-IID settings, DualFed achieves benign accuracies exceeding 84.0MNIST, 52.0dataset, while reducing malicious attack accuracy to near zero and outperforming recent defense baselines.
Hanene Hamdani, Emna Benmohamed, Hela Ltifi· International Conference on...· 0 citations
The challenge of cross-lingual semantic similarity detection is a significant problem in the context of multilingual educational software tools. This paper proposes a novel approach using ensemble learning and knowledge distillation for the development of an efficient and interpretable cross-lingual semantic similarity detection model for the English-French language pair. The methodology is based on the fusion of knowledge from the MiniLM encoder representation using a lightweight attention mechanism, LaBSE encoder with support for language-independent semantic representations, and the BERT encoder with the ability to produce dense contextual vector representations. The knowledge is then distilled using a Multi-Layer Perceptron (MLP) architecture for the development of the semantic similarity detection model. The experimental results show that the ensemble architecture attains a validation F1-score of 0.930, while the knowledge distillation student model retains a robust F1-score of 0.918 with a low computational footprint (3.17M parameters, 42MB memory, 5.77ms inference). This demonstrates the viability of knowledge distillation for the transfer of ensemble-level semantic knowledge into a compact architecture for the context of resource-constrained educational tools.
Mouna Khlifi, Samar Bouazizi, Hela Ltifi· International Conference on...· 0 citations
