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Found 25 papers, 0 papers with code
Aggressive or Imperceptible, or Both: Network Pruning Assisted Hybrid Byzantines in Federated Learning
Hence, inspired by the sparse neural networks, we introduce a hybrid sparse Byzantine attack that is composed of two parts: one exhibiting a sparse nature and attacking only certain NN locations with higher sensitivity, and the other being more silent but accumulating over time, where each ideally targets a different type of defence mechanism, and together they form a strong but imperceptible attack.
Process-and-Forward: Deep Joint Source-Channel Coding Over Cooperative Relay Networks
In the proposed scheme, the source transmits information in blocks, and the relay updates its knowledge about the input signal after each block and generates its own signal to be conveyed to the destination.
Feedback is Good, Active Feedback is Better: Block Attention Active Feedback Codes
Deep neural network (DNN)-assisted channel coding designs, such as low-complexity neural decoders for existing codes, or end-to-end neural-network-based auto-encoder designs are gaining interest recently due to their improved performance and flexibility; particularly for communication scenarios in which high-performing structured code designs do not exist.
Byzantines can also Learn from History: Fall of Centered Clipping in Federated Learning
The centered clipping (CC) framework has further shown that the momentum term from the previous iteration, besides reducing the variance, can be used as a reference point to neutralize Byzantine attacks better.
All you need is feedback: Communication with block attention feedback codes
Deep learning based channel code designs have recently gained interest as an alternative to conventional coding algorithms, particularly for channels for which existing codes do not provide effective solutions.
AttentionCode: Ultra-Reliable Feedback Codes for Short-Packet Communications
The training methods can potentially be generalized to other wireless communication applications with machine learning.
Semi-Decentralized Federated Learning with Collaborative Relaying
We present a semi-decentralized federated learning algorithm wherein clients collaborate by relaying their neighbors' local updates to a central parameter server (PS).
Federated Spatial Reuse Optimization in Next-Generation Decentralized IEEE 802.11 WLANs
As wireless standards evolve, more complex functionalities are introduced to address the increasing requirements in terms of throughput, latency, security, and efficiency.
Robust Federated Learning with Connectivity Failures: A Semi-Decentralized Framework with Collaborative Relaying
Intermittent connectivity of clients to the parameter server (PS) is a major bottleneck in federated edge learning frameworks.
Collaborative Learning over Wireless Networks: An Introductory Overview
This is partly due to the communication bottleneck limiting the overall computation speed.
Less is More: Feature Selection for Adversarial Robustness with Compressive Counter-Adversarial Attacks
Hence, we propose a novel approach to identify the important features by employing counter-adversarial attacks, which highlights the consistency at the penultimate layer with respect to perturbations on input samples.
Gradient Coding with Dynamic Clustering for Straggler-Tolerant Distributed Learning
Distributed implementations are crucial in speeding up large scale machine learning applications.
Time-Correlated Sparsification for Communication-Efficient Federated Learning
Sparse communication is often employed to reduce the communication load, where only a small subset of the model updates are communicated from the clients to the PS.
FedADC: Accelerated Federated Learning with Drift Control
The core of the FL strategy is the use of stochastic gradient descent (SGD) in a distributed manner.
Distributed Sparse SGD with Majority Voting
However, top-K sparsification requires additional communication load to represent the sparsity pattern, and the mismatch between the sparsity patterns of the workers prevents exploitation of efficient communication protocols.
Gradient Coding with Dynamic Clustering for Straggler Mitigation
In distributed synchronous gradient descent (GD) the main performance bottleneck for the per-iteration completion time is the slowest \textit{straggling} workers.
Communicate to Learn at the Edge
Bringing the success of modern machine learning (ML) techniques to mobile devices can enable many new services and businesses, but also poses significant technical and research challenges.
Coded Distributed Computing with Partial Recovery
In this paper, we first introduce a novel coded matrix-vector multiplication scheme, called coded computation with partial recovery (CCPR), which benefits from the advantages of both coded and uncoded computation schemes, and reduces both the computation time and the decoding complexity by allowing a trade-off between the accuracy and the speed of computation.
Age-Based Coded Computation for Bias Reduction in Distributed Learning
To mitigate biased estimators, we design a $timely$ dynamic encoding framework for partial recovery that includes an ordering operator that changes the codewords and computation orders at workers over time.
Straggler-aware Distributed Learning: Communication Computation Latency Trade-off
When gradient descent (GD) is scaled to many parallel workers for large scale machine learning problems, its per-iteration computation time is limited by the straggling workers.
Decentralized SGD with Over-the-Air Computation
We study the performance of decentralized stochastic gradient descent (DSGD) in a wireless network, where the nodes collaboratively optimize an objective function using their local datasets.
Hierarchical Federated Learning Across Heterogeneous Cellular Networks
We study collaborative machine learning (ML) across wireless devices, each with its own local dataset.
Gradient Coding with Clustering and Multi-message Communication
Gradient descent (GD) methods are commonly employed in machine learning problems to optimize the parameters of the model in an iterative fashion.
Distributed Gradient Descent with Coded Partial Gradient Computations
Coded computation techniques provide robustness against straggling servers in distributed computing, with the following limitations: First, they increase decoding complexity.
Speeding Up Distributed Gradient Descent by Utilizing Non-persistent Stragglers
In most of the existing DGD schemes, either with coded computation or coded communication, the non-straggling CSs transmit one message per iteration once they complete all their assigned computation tasks.
