25th Annual Conference on Learning Theory Distributed Learning, Communication Complexity and Privacy

Subfields: Statistical Learning Theory, Privacy & Security

Citations in the corpus

Federated Optimization: Distributed Optimization Beyond the Datacenter (2015, background): “Works including [2, 8, 3, 9] have established basic theoretical results and developed distributed versions of existing methods, but as this is a relatively new area, many open questions remain.”
Distributed Simulation and Distributed Inference (2018, background): “The problem described above has itself received signiﬁcant attention lately in various communities (see, for instance, [BPC + 11, BBFM12, Sha14, DGL + 17, HÖW18]), with the objective of performing parameter or density estimation while minimizing the number of players (or, equivalently, the amount…”
Geometric Lower Bounds for Distributed Parameter Estimation Under Communication Constraints (2018, background): “…also an increasing number of applications in which data is generated in a distributed manner and it (or features of it) are communicated over bandwidth-limited links to central processors Boyd et al. (2011); Balcan et al. (2012); Daume III et al. (2012); Daumé et al. (2012); Dekel et al. (2012).”
Distributed Mean Estimation with Limited Communication (2016, background): “This has spurred a line of work focusing on communication cost in learning (Tsitsiklis & Luo, 1987; Balcan et al., 2012; Zhang et al., 2013; Arjevani & Shamir, 2015; Chen et al., 2016).”
Communication-Efficient Learning of Deep Networks from Decentralized Data (2016, background): “In the convex setting, the problem of distributed optimization and estimation has received significant attention [4, 15, 33], and some algorithms do focus specifically on communication efficiency [45, 34, 40, 27, 43].”
LoAdaBoost: Loss-based AdaBoost federated machine learning with reduced computational complexity on IID and non-IID intensive care data (2018, methodology): “arning was involved in these two models, their practice of distributed data management and retrieval served as a reference for the development of federated learning algorithms. In 2012, Balcan et al. [3] implemented probably approximately correct (PAC) learning in a federated manner and reported the”
CoCoA: A General Framework for Communication-Efficient Distributed Optimization (2016, background): “Additional relevant lower bounds on the minimum number of communication rounds necessary for a given approximation quality are presented in Balcan et al. (2012) and Arjevani and Shamir (2015).”
Adding vs. Averaging in Distributed Primal-Dual Optimization (2015, background): “For an overview over the currently active research ﬁeld, we refer the reader to (Balcan et al., 2012; Richt ´ arik & Tak ´ a ˇ c, 2013b; Duchi et al., 2013; Yang, 2013; ?”
Communication Complexity of Distributed Convex Learning and Optimization (2015, background): “As datasets continue to increase in size, and parallel computing platforms becoming more and more common (from multiple cores on a single CPU to large-scale and geographically distributed computing grids), distributed learning and optimization methods have been the focus of much research in recent y”
Communication-Efficient Distributed Dual Coordinate Ascent (2014, background): “We see that thoughβK has a larger impact on the smaller batch size, it is still not enough to improve the mini-batch algorithms beyond what is achieved by COCOA and local-SGD.”
Distributed optimization with arbitrary local solvers (2015, background): “…with the massive growth of available datasets, major roadblocks still persist in the distributed setting, where data no longer ﬁts in the memory of a single computer, and computation must be split across multiple machines in a network [3, 7, 12, 18, 22, 28, 32, 34, 37, 46, 50, 58, 60, 62, 76].”
On Communication Cost of Distributed Statistical Estimation and Dimensionality (2014, background): “A lot of recent works are devoted to understand how to solve problems distributedly with efficient communication [2, 3, 4, 1, 5].”
Fundamental Limits of Online and Distributed Algorithms for Statistical Learning and Estimation (2013, background): “[6, 29]) we can even formalize the price we pay for these constraints, in terms of degraded sample complexity or regret guarantees.”
Distributed Multi-Task Learning (2015, methodology): “Again, the problem becomes harder when more machines are involved and one can obtain lower bounds on the amount of communication required—[Balcan et al., 2012] carry out such an analysis for several hypothesis classes.”
Hadamard Response: Estimating Distributions Privately, Efficiently, and with Little Communication (2018, background): “Communication limited distributed distribution estimation has been studied in the non-private setting(e.g., [48, 4, 19, 2, 28]).”
Accelerated Mini-Batch Stochastic Dual Coordinate Ascent (2013, background): “Cotter et al. [2011] studied SGD and accelerated versions of SGD with mini-batches and Takác et al. [2013] studied SDCA with minibatches for SVMs. Duchi et al. [2010] studied dual averaging in distributed networks as a function of spectral properties of the underlying graph.”
Stochastic, Distributed and Federated Optimization for Machine Learning (2017, background): “However, with the massive growth of available datasets, major roadblocks still persist in the distributed setting, where data no longer fits in the memory of a single computer, and computation must be split across multiple machines in a network [19, 111, 106, 8, 149, 53, 173, 57, 32, 108, 166, 167, ”
Inference Under Information Constraints II: Communication Constraints and Shared Randomness (2019, background): “The problem of distributed density estimation, too, has gathered recent interest in various statistical settings [5], [9], [14], [22], [33], [42], [43], [48]–[50].”