Federated learning has allowed training of a global model by aggregating local models trained on local nodes.
In Federated Learning (FL), to leverage knowledge from different domains, learned model parameters are shared to train a global model.
To overcome this challenge, we propose the Attacking Distance-aware Attack (ADA) to enhance a poisoning attack by finding the optimized target class in the feature space.
Ranked #1 on Model Poisoning on Fashion-MNIST
We collected the most recent phishing samples to study the effectiveness of the proposed method using different client numbers and data distributions.
To this end, we propose a decentralized learning model called Homogeneous Learning (HL) for tackling non-IID data with a self-attention mechanism.
At last, we successfully reconstructed the real data of the victim from the shared global model parameters with all the applied datasets.
Wider coverage and a better solution to a latency reduction in 5G necessitate its combination with multi-access edge computing (MEC) technology.
We propose Segmented-Federated Learning (Segmented-FL), where by employing periodic local model evaluation and network segmentation, we aim to bring similar network environments to the same group.
In this research, a segmented federated learning is proposed, different from a collaborative learning based on single global model in a traditional federated learning model, it keeps multiple global models which allow each segment of participants to conduct collaborative learning separately and rearranges the segmentation of participants dynamically as well.
Ranked #1 on Network Intrusion Detection on SIDD-Image