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Found 12 papers, 3 papers with code
Confidential Federated Computations
Federated Learning and Analytics (FLA) have seen widespread adoption by technology platforms for processing sensitive on-device data.
Gradient Descent with Linearly Correlated Noise: Theory and Applications to Differential Privacy
We propose a simplified setting that distills key facets of these methods and isolates the impact of linearly correlated noise.
Federated Select: A Primitive for Communication- and Memory-Efficient Federated Learning
In order to make this practical, we outline a primitive, federated select, which enables client-specific selection in realistic FL systems.
Improved Differential Privacy for SGD via Optimal Private Linear Operators on Adaptive Streams
Motivated by recent applications requiring differential privacy over adaptive streams, we investigate the question of optimal instantiations of the matrix mechanism in this setting.
Practical and Private (Deep) Learning without Sampling or Shuffling
We consider training models with differential privacy (DP) using mini-batch gradients.
Federated Learning with Autotuned Communication-Efficient Secure Aggregation
Federated Learning enables mobile devices to collaboratively learn a shared inference model while keeping all the training data on a user's device, decoupling the ability to do machine learning from the need to store the data in the cloud.
Graph Oracle Models, Lower Bounds, and Gaps for Parallel Stochastic Optimization
We suggest a general oracle-based framework that captures different parallel stochastic optimization settings described by a dependency graph, and derive generic lower bounds in terms of this graph.
Federated Optimization:Distributed Optimization Beyond the Datacenter
We refer to this setting as Federated Optimization.
Delay-Tolerant Algorithms for Asynchronous Distributed Online Learning
We analyze new online gradient descent algorithms for distributed systems with large delays between gradient computations and the corresponding updates.
Minimax Optimal Algorithms for Unconstrained Linear Optimization
We design and analyze minimax-optimal algorithms for online linear optimization games where the player's choice is unconstrained.
Estimation, Optimization, and Parallelism when Data is Sparse
We study stochastic optimization problems when the \emph{data} is sparse, which is in a sense dual to the current understanding of high-dimensional statistical learning and optimization.
No-Regret Algorithms for Unconstrained Online Convex Optimization
We present an algorithm that, without such prior knowledge, offers near-optimal regret bounds with respect to _any_ choice of x*.
