Emerging technologies and applications including Internet of Things (IoT), social networking, and crowd-sourcing generate large amounts of data at the network edge. Machine learning models are often built from the collected data, to enable the detection, classification, and prediction of future events. Due to bandwidth, storage, and privacy concerns, it is often impractical to send all the data to a centralized location. In this paper, we consider the problem of learning model parameters from data distributed across multiple edge nodes, without sending raw data to a centralized place. Our focus is on a generic class of machine learning models that are trained using gradientdescent based approaches. We analyze the convergence bound of distributed gradient descent from a theoretical point of view, based on which we propose a control algorithm that determines the best trade-off between local update and global parameter aggregation to minimize the loss function under a given resource budget. The performance of the proposed algorithm is evaluated via extensive experiments with real datasets, both on a networked prototype system and in a larger-scale simulated environment. The experimentation results show that our proposed approach performs near to the optimum with various machine learning models and different data distributions.

Emerging technologies and applications including Internet of Things (IoT), social networking, and crowd-sourcing generate large amounts of data at the network edge. Machine learning models are often built from the collected data, to enable the detection, classification, and prediction of future events. Due to bandwidth, storage, and privacy concerns, it is often impractical to send all the data to a centralized location. In this paper, we consider the problem of learning model parameters from data distributed across multiple edge nodes, without sending raw data to a centralized place. Our focus is on a generic class of machine learning models that are trained using gradientdescent based approaches. We analyze the convergence rate of distributed gradient descent from a theoretical point of view, based on which we propose a control algorithm that determines the best trade-off between local update and global parameter aggregation to minimize the loss function under a given resource budget. The performance of the proposed algorithm is evaluated via extensive experiments with real datasets, both on a networked prototype system and in a larger-scale simulated environment. The experimentation results show that our proposed approach performs near to the optimum with various machine learning models and different data distributions.

No abstract

Abstract-Multi-hop wireless networks employing random access protocols have been shown to incur large discrepancies in the throughputs achieved by the flows sharing the network. Indeed, flow throughputs can span orders of magnitude from near starvation to many times greater than the mean. In this paper, we address the foundations of this disparity. We show that the fundamental cause is not merely differences in the number of contending neighbors, but a generic coordination problem of CSMA-based random access in a multi-hop environment. We develop a new analytical model that incorporates this lack of coordination, identifies dominating and starving flows and accurately predicts per-flow throughput in a large-scale network. We then propose metrics that quantify throughput imbalances due to the MAC protocol operation. Our model and metrics provide a deeper understanding of the behavior of CSMA protocols in arbitrary topologies and can aid the design of effective protocol solutions to the starvation problem.

In recent years, wireless ad hoc networks have been a growing area of research. While there has been considerable research on the topic of routing in such networks, the topic of topology creation has not received due attention. This is because almost all ad hoc networks to date have been built on top of a single channel, broadcast based wireless media, such as 802.11 or IR LANs. For such networks the distance relationship between the nodes implicitly (and uniquely) determines the topology of the ad hoc network.Bluetooth is a promising new wireless technology, which enables portable devices to form short-range wireless ad hoc networks and is based on a frequency hopping physical layer. This fact implies that hosts are not able to communicate unless they have previously discovered each other by synchronizing their frequency hopping patterns. Thus, even if all nodes are within direct communication range of each other, only those nodes which are synchronized with the transmitter can hear the transmission. To support any-to-any communication, nodes must be synchronized so that the pairs of nodes (which can communicate with each other) together form a connected graph.Using Bluetooth as an example, this paper first provides deeper insights into the issue to link establishment in frequency hopping wireless systems. It then introduces the Bluetooth Topology Costruction Protocol (BTCP), an asynchronous distributed protocol for constructing scatternets which starts with nodes that have no knowledge of their surroundings and terminates with the formation of a connected network satisfying all connectivity constraints posed by the Bluetooth technology. To the best of our knowledge, the work presented in this paper is the first attempt at building Bluetooth scatternets using distributed logic and is quite "practical" in the sense that it can be implemented using the communication primitives offered by the Bluetooth 1.0 specifications.