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Preserving Sparsity and Privacy in Straggler-Resilient Distributed Matrix Computations
Anindya Bijoy Das, Aditya Ramamoorthy, David J. Love +1
Existing approaches to distributed matrix computations involve allocating coded combinations of submatrices to worker nodes, to build resilience to stragglers and/or enhance privac…
Derandomizing Codes for the Binary Adversarial Wiretap Channel of Type II
Eric Ruzomberka, Homa Nikbakht, Christopher G. Brinton +2
We revisit the binary adversarial wiretap channel (AWTC) of type II in which an active adversary can read a fraction and flip a fraction of codeword bits. The semantic-secr…
Adversarial Channels with O(1)-Bit Partial Feedback
Eric Ruzomberka, Yongkyu Jang, David J. Love +1
We consider point-to-point communication over -ary adversarial channels with partial noiseless feedback. In this setting, a sender Alice transmits symbols from a -ary alp…
Robust Non-Linear Feedback Coding via Power-Constrained Deep Learning
Junghoon Kim, Taejoon Kim, David Love +1
The design of codes for feedback-enabled communications has been a long-standing open problem. Recent research on non-linear, deep learning-based coding schemes have demonstrated s…
Coded Matrix Computations for D2D-enabled Linearized Federated Learning
Anindya Bijoy Das, Aditya Ramamoorthy, David J. Love +1
Federated learning (FL) is a popular technique for training a global model on data distributed across client devices. Like other distributed training techniques, FL is susceptible…
Distributed Matrix Computations with Low-weight Encodings
Anindya Bijoy Das, Aditya Ramamoorthy, David J. Love +1
Straggler nodes are well-known bottlenecks of distributed matrix computations which induce reductions in computation/communication speeds. A common strategy for mitigating such str…