319,305 research outputs found
Federated Meta Learning Enhanced Acoustic Radio Cooperative Framework for Ocean of Things Underwater Acoustic Communications
Sixth-generation wireless communication (6G) will be an integrated
architecture of "space, air, ground and sea". One of the most difficult part of
this architecture is the underwater information acquisition which need to
transmitt information cross the interface between water and air.In this
senario, ocean of things (OoT) will play an important role, because it can
serve as a hub connecting Internet of things (IoT) and Internet of underwater
things (IoUT). OoT device not only can collect data through underwater methods,
but also can utilize radio frequence over the air. For underwater
communications, underwater acoustic communications (UWA COMMs) is the most
effective way for OoT devices to exchange information, but it is always
tormented by doppler shift and synchronization errors. In this paper, in order
to overcome UWA tough conditions, a deep neural networks based receiver for
underwater acoustic chirp communication, called C-DNN, is proposed. Moreover,
to improve the performance of DL-model and solve the problem of model
generalization, we also proposed a novel federated meta learning (FML) enhanced
acoustic radio cooperative (ARC) framework, dubbed ARC/FML, to do transfer.
Particularly, tractable expressions are derived for the convergence rate of FML
in a wireless setting, accounting for effects from both scheduling ratio, local
epoch and the data amount on a single node.From our analysis and simulation
results, it is shown that, the proposed C-DNN can provide a better BER
performance and lower complexity than classical matched filter (MF) in
underwater acoustic communications scenario. The ARC/FML framework has good
convergence under a variety of channels than federated learning (FL). In
summary, the proposed ARC/FML for OoT is a promising scheme for information
exchange across water and air
Distributed learning and inference in deep models
In recent years, the size of deep learning problems has been increased significantly, both in terms of the number of available training samples as well as the number of parameters and complexity of the model. In this thesis, we considered the challenges encountered in training and inference of large deep models, especially on nodes with limited computational power and capacity. We studied two classes of related problems; 1) distributed training of deep models, and 2) compression and restructuring of deep models for efficient distributed and parallel execution to reduce inference times. Especially, we considered the communication bottleneck in distributed training and inference of deep models. Data compression is a viable tool to mitigate the communication bottleneck in distributed deep learning. However, the existing methods suffer from a few drawbacks, such as the increased variance of stochastic gradients (SG), slower convergence rate, or added bias to SG. In my Ph.D. research, we have addressed these challenges from three different perspectives: 1) Information Theory and the CEO Problem, 2) Indirect SG compression via Matrix Factorization, and 3) Quantized Compressive Sampling. We showed, both theoretically and via simulations, that our proposed methods can achieve smaller MSE than other unbiased compression methods with fewer communication bit-rates, resulting in superior convergence rates. Next, we considered federated learning over wireless multiple access channels (MAC). Efficient communication requires the communication algorithm to satisfy the constraints imposed by the nodes in the network and the communication medium. To satisfy these constraints and take advantage of the over-the-air computation inherent in MAC, we proposed a framework based on random linear coding and developed efficient power management and channel usage techniques to manage the trade-offs between power consumption and communication bit-rate. In the second part of this thesis, we considered the distributed parallel implementation of an already-trained deep model on multiple workers. Since latency due to the synchronization and data transfer among workers adversely affects the performance of the parallel implementation, it is desirable to have minimum interdependency among parallel sub-models on the workers. To achieve this goal, we developed and analyzed RePurpose, an efficient algorithm to rearrange the neurons in the neural network and partition them (without changing the general topology of the neural network) such that the interdependency among sub-models is minimized under the computations and communications constraints of the workers.Ph.D
Guided Deep Reinforcement Learning for Swarm Systems
In this paper, we investigate how to learn to control a group of cooperative
agents with limited sensing capabilities such as robot swarms. The agents have
only very basic sensor capabilities, yet in a group they can accomplish
sophisticated tasks, such as distributed assembly or search and rescue tasks.
Learning a policy for a group of agents is difficult due to distributed partial
observability of the state. Here, we follow a guided approach where a critic
has central access to the global state during learning, which simplifies the
policy evaluation problem from a reinforcement learning point of view. For
example, we can get the positions of all robots of the swarm using a camera
image of a scene. This camera image is only available to the critic and not to
the control policies of the robots. We follow an actor-critic approach, where
the actors base their decisions only on locally sensed information. In
contrast, the critic is learned based on the true global state. Our algorithm
uses deep reinforcement learning to approximate both the Q-function and the
policy. The performance of the algorithm is evaluated on two tasks with simple
simulated 2D agents: 1) finding and maintaining a certain distance to each
others and 2) locating a target.Comment: 15 pages, 8 figures, accepted at the AAMAS 2017 Autonomous Robots and
Multirobot Systems (ARMS) Worksho
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