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Found 9 papers, 0 papers with code
Maximal-Capacity Discrete Memoryless Channel Identification
Based on this capacity estimator, a gap-elimination algorithm termed BestChanID is proposed, which is oblivious to the capacity-achieving input distribution and is guaranteed to output the DMC with the largest capacity, with a desired confidence.
Private Aggregation in Wireless Federated Learning with Heterogeneous Clusters
Federated learning collaboratively trains a neural network on privately owned data held by several participating clients.
Fast and Straggler-Tolerant Distributed SGD with Reduced Computation Load
Considering this model, we construct a novel scheme that adapts both the number of workers and the computation load throughout the run-time of the algorithm.
Nested Gradient Codes for Straggler Mitigation in Distributed Machine Learning
Since the number of stragglers in practice is random and unknown a priori, tolerating a fixed number of stragglers can yield a sub-optimal computation load and can result in higher latency.
Adaptive Stochastic Gradient Descent for Fast and Communication-Efficient Distributed Learning
Moreover, the results also show that the adaptive version is communication-efficient, where the amount of communication required between the master and the workers is less than that of non-adaptive versions.
Cost-Efficient Distributed Learning via Combinatorial Multi-Armed Bandits
We consider the distributed SGD problem, where a main node distributes gradient calculations among $n$ workers.
Multiple Criss-Cross Insertion and Deletion Correcting Codes
We show an equivalence between correcting $t$-criss-cross deletions and $t$-criss-cross insertions and show that a code correcting $t$-criss-cross insertions/deletions has redundancy at least $tn + t \log n - \log(t!)$.
Information Theory Information Theory
Adaptive Private Distributed Matrix Multiplication
A master server owns two private matrices $\mathbf{A}$ and $\mathbf{B}$ and hires worker nodes to help computing their multiplication.
Information Theory Information Theory
Adaptive Distributed Stochastic Gradient Descent for Minimizing Delay in the Presence of Stragglers
One solution studied in the literature is to wait at each iteration for the responses of the fastest $k<n$ workers before updating the model, where $k$ is a fixed parameter.
