15 papers
Game of Coding under Computation-Dependent Adversarial Noise
Hanzaleh Akbari Nodehi, Mohammad Ali Maddah-Ali
The game of coding framework was introduced to extend coding-theoretic recovery beyond its traditional limit, under which the number of honest reports must exceed the number of adv…
\texttt{Range-Arithmetic}: Verifiable Deep Learning Inference on an Untrusted Party
Ali Rahimi, Babak H. Khalaj, Mohammad Ali Maddah-Ali
Verifiable computing (VC) has gained prominence in decentralized machine learning systems, where resource-intensive tasks like deep neural network (DNN) inference are offloaded to…
Learning from Acceptance: Cumulative Regret in the Game of Coding
Hanzaleh Akbari Nodehi, Parsa Moradi, Mohammad Ali Maddah-Ali
Classical coding-theoretic guarantees often rely on trust assumptions, such as requiring sufficiently many honest nodes compared with adversarial ones. These assumptions are diffic…
\mathsf{VISTA}: Decentralized Machine Learning in Adversary Dominated Environments
Hanzaleh Akbari Nodehi, Parsa Moradi, Soheil Mohajer +1
Decentralized machine learning often relies on outsourcing computations, such as gradient evaluations, to untrusted worker nodes. Existing robust aggregation methods can mitigate m…
DReS: Dual Reconstruction Smoothing for Functional Regularization
Parsa Moradi, Tayyebeh Jahaninezhad, Hanzaleh Akbarinodehi +1
Smoothness is a key inductive bias in machine learning and is closely related to generalization. Existing smoothness-inducing methods typically rely either on explicit gradient reg…
Game of Coding for Vector-Valued Computations
Hanzaleh Akbari Nodehi, Parsa Moradi, Soheil Mohajer +1
Traditional coding theory guarantees valid decoding only if a minority of symbols are adversarially manipulated. In contrast, the game of coding framework ensures reliable decoding…