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Found 7 papers, 2 papers with code
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.
Walk for Learning: A Random Walk Approach for Federated Learning from Heterogeneous Data
One of the main challenges of FL is the communication bottleneck between the nodes and the parameter server.
Private Weighted Random Walk Stochastic Gradient Descent
To speed up the convergence, we propose instead to study random walk based SGD in which a global model is updated based on a random walk on the graph.
Mechanisms for Hiding Sensitive Genotypes with Information-Theoretic Privacy
Motivated by the growing availability of personal genomics services, we study an information-theoretic privacy problem that arises when sharing genomic data: a user wants to share his or her genome sequence while keeping the genotypes at certain positions hidden, which could otherwise reveal critical health-related information.
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.
Advances and Open Problems in Federated Learning
FL embodies the principles of focused data collection and minimization, and can mitigate many of the systemic privacy risks and costs resulting from traditional, centralized machine learning and data science approaches.
Guess & Check Codes for Deletions, Insertions, and Synchronization
We consider the problem of constructing codes that can correct $\delta$ deletions occurring in an arbitrary binary string of length $n$ bits.
Information Theory Information Theory
