The Lebesgue measure of ε-forging sets decays as O(ε) or ε^d for linear models and as ε^{(d-r)/2} under mild regularity assumptions, with vanishing probability of random sampling.
Springer Science & Business Media
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An active reputation learning mechanism integrated into consensus protocols enables simultaneous Byzantine agent identification and resilient agreement among normal agents in distributed systems.
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The Measure of Deception: An Analysis of Data Forging in Machine Unlearning
The Lebesgue measure of ε-forging sets decays as O(ε) or ε^d for linear models and as ε^{(d-r)/2} under mild regularity assumptions, with vanishing probability of random sampling.
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Byzantine-Resilient Consensus via Active Reputation Learning
An active reputation learning mechanism integrated into consensus protocols enables simultaneous Byzantine agent identification and resilient agreement among normal agents in distributed systems.