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Found 8 papers, 1 papers with code
Leak and Learn: An Attacker's Cookbook to Train Using Leaked Data from Federated Learning
We demonstrate the effectiveness of both GI and LLL attacks in maliciously training models using the leaked data more accurately than a benign federated learning strategy.
Position Control of Single Link Flexible Manipulator: A Functional Observer Based Sliding Mode Approach
This paper proposes a functional observer-based sliding mode control technique for position control of a single-link flexible manipulator.
The Resource Problem of Using Linear Layer Leakage Attack in Federated Learning
We show that this resource overhead is caused by an incorrect perspective in all prior work that treats an attack on an aggregate update in the same way as an individual update with a larger batch size.
LOKI: Large-scale Data Reconstruction Attack against Federated Learning through Model Manipulation
When both FedAVG and secure aggregation are used, there is no current method that is able to attack multiple clients concurrently in a federated learning setting.
Lerna: Transformer Architectures for Configuring Error Correction Tools for Short- and Long-Read Genome Sequencing
We also show that the alignment rate and assembly quality computed for the corrected reads are strongly negatively correlated with the perplexity, enabling the automated selection of k-mer values for better error correction, and hence, improved assembly quality.
TESSERACT: Gradient Flip Score to Secure Federated Learning Against Model Poisoning Attacks
The attack uses the intuition that simply by changing the sign of the gradient updates that the optimizer is computing, for a set of malicious clients, a model can be diverted from the optima to increase the test error rate.
Detection of Plant Leaf Disease Directly in the JPEG Compressed Domain using Transfer Learning Technique
Therefore accurate and timely detection of leaf disease is very important to check the loss of the crops and meet the growing food demand of the people.
Performance Based Evaluation of Various Machine Learning Classification Techniques for Chronic Kidney Disease Diagnosis
In order to calculate efficiency, results of the prediction by candidate methods were compared with the actual medical results of the subject. The various metrics used for performance evaluation are predictive accuracy, precision, sensitivity and specificity.
