A combinatorial optimization problem is formulated to provide the optimal practical-repair-bandwidth for a given packet erasure probability. We study the minimum practical-repair-bandwidth, i.e., the repair-bandwidth for achieving a given probability of successful repair.
Finally, we study the repair in a nonasymptotic setup, where the stored file size is finite. Next, we show the benefits of DR storage nodes in reducing the repair bandwidth, and then we derive the necessary minimal storage space of DR storage nodes. The result shows that the asymptotic repair-bandwidth over packet erasure channels with a fixed erasure probability has a closed-form relation to the repair-bandwidth in lossless networks. We first investigate the minimum required repair-bandwidth in an asymptotic setup, in which the stored file is assumed to have an infinite size. We study the repair problem in distributed storage systems where storage nodes are connected through packet erasure channels and some nodes are dedicated to repair. The efficiency of our method is demonstrated by a study of causal effects of oxygen saturation on hospital mortality and backed up by comprehensive numerical results. Drawing on a multi-hospital electronic health records network, we develop an efficient and interpretable tree-based ensemble of personalized treatment effect estimators to join results across hospital sites, while actively modeling for the heterogeneity in data sources through site partitioning. However, existing federated learning methods mainly assume data across sites are homogeneous samples of the global population, hence failing to properly account for the extra variability across sites in estimation and inference. Under this framework, data partners at local sites collaboratively build an analytical model under the orchestration of a coordinating site, while keeping the data decentralized. Our results show a good agreement between analysis and simulations, thus confirming the effectiveness of our network coding-based approach.įederated learning is an appealing framework for analyzing sensitive data from distributed health data networks due to its protection of data privacy.
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In the current paper, we provide a preliminary analytical framework that encompasses the use of a few characteristic parameters of the considered distributed storage system, such as the resource availability and the available free disk space portion. In fact, the proposed network coding-based scheme provides proactive network maintenance to guarantee sufficient resource redundancy: this leads to a reduction of maintenance complexity and to a larger amount of free disk space on storage nodes.
This system has been introduced and studied, by means of simulations, in a previous paper, where the use of randomized network coding has been proposed as an appealing alternative to classical erasure coding to generate the required redundancy in a distributed storage system.
In this paper, we analyze the performance of a peer-to-peer (P2P) distributed storage architecture based on a layered overlay scheme, where some nodes provide their disk capacities for hosting data fragments generated by other active users.