Impact of Trust Management and Information Sharing to Adversarial Cost in Ranking Systems

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Federated Learning by nature is susceptible to low-quality, corrupted, or even malicious data that can severely degrade the quality of the learned model. Traditional techniques for data valuation cannot be applied as the data is never revealed. We present ...

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Impact of Trust Management and Information Sharing to Adversarial Cost in Ranking Systems

Chattez avec Graph Search

X-Attack 2.0: The Risk of Power Wasters and Satisfiability Don’t-Care Hardware Trojans to Shared Cloud FPGAs

Exploring High-Performance and Energy-Efficient Architectures for Edge AI-Enabled Applications

Exciton migration in two-dimensional materials

Mechanically induced correlated errors on superconducting qubits with relaxation times exceeding 0.4 ms

A Method of Moments Estimator for Interacting Particle Systems and their Mean Field Limit

A Practical Influence Approximation for Privacy-Preserving Data Filtering in Federated Learning

Dynamism in the context of views out: A literature review

incentive Mechanism Design for Responsible Data Governance: A Large-Scale Field Experiment

Caching and Neutrality

On the Performance of Subjective Visual Quality Assessment Protocols for Nearly Visually Lossless Image Compression