Wireless Federated $k$-Means Clustering with Non-coherent Over-the-Air
Computation
- URL: http://arxiv.org/abs/2308.06371v1
- Date: Fri, 11 Aug 2023 20:12:26 GMT
- Title: Wireless Federated $k$-Means Clustering with Non-coherent Over-the-Air
Computation
- Authors: Alphan Sahin
- Abstract summary: OAC scheme relies on an encoder exploiting the representation of a number in a balanced number system.
Reinitialization method for ineffectively used centroids is proposed to improve the performance of the proposed method for heterogeneous data distribution.
- Score: 14.087062902871212
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: In this study, we propose using an over-the-air computation (OAC) scheme for
the federated k-means clustering algorithm to reduce the per-round
communication latency when it is implemented over a wireless network. The OAC
scheme relies on an encoder exploiting the representation of a number in a
balanced number system and computes the sum of the updates for the federated
k-means via signal superposition property of wireless multiple-access channels
non-coherently to eliminate the need for precise phase and time
synchronization. Also, a reinitialization method for ineffectively used
centroids is proposed to improve the performance of the proposed method for
heterogeneous data distribution. For a customer-location clustering scenario,
we demonstrate the performance of the proposed algorithm and compare it with
the standard k-means clustering. Our results show that the proposed approach
performs similarly to the standard k-means while reducing communication
latency.
Related papers
- Boosting the Performance of Decentralized Federated Learning via Catalyst Acceleration [66.43954501171292]
We introduce Catalyst Acceleration and propose an acceleration Decentralized Federated Learning algorithm called DFedCata.
DFedCata consists of two main components: the Moreau envelope function, which addresses parameter inconsistencies, and Nesterov's extrapolation step, which accelerates the aggregation phase.
Empirically, we demonstrate the advantages of the proposed algorithm in both convergence speed and generalization performance on CIFAR10/100 with various non-iid data distributions.
arXiv Detail & Related papers (2024-10-09T06:17:16Z) - Self-Supervised Graph Embedding Clustering [70.36328717683297]
K-means one-step dimensionality reduction clustering method has made some progress in addressing the curse of dimensionality in clustering tasks.
We propose a unified framework that integrates manifold learning with K-means, resulting in the self-supervised graph embedding framework.
arXiv Detail & Related papers (2024-09-24T08:59:51Z) - Adaptively Robust and Sparse K-means Clustering [5.535948428518607]
This paper proposes adaptively robust and sparse K-means clustering (ARSK) to address these practical limitations of the standard K-means algorithm.
For robustness, we introduce a redundant error component for each observation, and this additional parameter is penalized using a group sparse penalty.
To accommodate the impact of high-dimensional noisy variables, the objective function is modified by incorporating weights and implementing a penalty to control the sparsity of the weight vector.
arXiv Detail & Related papers (2024-07-09T15:20:41Z) - Fuzzy K-Means Clustering without Cluster Centroids [21.256564324236333]
Fuzzy K-Means clustering is a critical technique in unsupervised data analysis.
This paper proposes a novel Fuzzy textitK-Means clustering algorithm that entirely eliminates the reliance on cluster centroids.
arXiv Detail & Related papers (2024-04-07T12:25:03Z) - Rethinking Clustered Federated Learning in NOMA Enhanced Wireless
Networks [60.09912912343705]
This study explores the benefits of integrating the novel clustered federated learning (CFL) approach with non-independent and identically distributed (non-IID) datasets.
A detailed theoretical analysis of the generalization gap that measures the degree of non-IID in the data distribution is presented.
Solutions to address the challenges posed by non-IID conditions are proposed with the analysis of the properties.
arXiv Detail & Related papers (2024-03-05T17:49:09Z) - Asynchronous Distributed Reinforcement Learning for LQR Control via Zeroth-Order Block Coordinate Descent [7.6860514640178]
We propose a novel zeroth-order optimization algorithm for distributed reinforcement learning.
It allows each agent to estimate its local gradient by cost evaluation independently, without use of any consensus protocol.
arXiv Detail & Related papers (2021-07-26T18:11:07Z) - Unsupervised Clustered Federated Learning in Complex Multi-source
Acoustic Environments [75.8001929811943]
We introduce a realistic and challenging, multi-source and multi-room acoustic environment.
We present an improved clustering control strategy that takes into account the variability of the acoustic scene.
The proposed approach is optimized using clustering-based measures and validated via a network-wide classification task.
arXiv Detail & Related papers (2021-06-07T14:51:39Z) - (k, l)-Medians Clustering of Trajectories Using Continuous Dynamic Time
Warping [57.316437798033974]
In this work we consider the problem of center-based clustering of trajectories.
We propose the usage of a continuous version of DTW as distance measure, which we call continuous dynamic time warping (CDTW)
We show a practical way to compute a center from a set of trajectories and subsequently iteratively improve it.
arXiv Detail & Related papers (2020-12-01T13:17:27Z) - Fast Convergence Algorithm for Analog Federated Learning [30.399830943617772]
We propose an AirComp-based FedSplit algorithm for efficient analog federated learning over wireless channels.
We prove that the proposed algorithm linearly converges to the optimal solutions under the assumption that the objective function is strongly convex and smooth.
Our algorithm is theoretically and experimentally verified to be much more robust to the ill-conditioned problems with faster convergence compared with other benchmark FL algorithms.
arXiv Detail & Related papers (2020-10-30T10:59:49Z) - Cluster-Based Cooperative Digital Over-the-Air Aggregation for Wireless
Federated Edge Learning [9.179817518536545]
We study a federated learning system at the wireless edge that uses over-the-air computation (AirComp)
In such a system, users transmit their messages over a multi-access channel concurrently to achieve fast model aggregation.
We propose an improved digital AirComp scheme to relax its requirements on the transmitters, where users perform phase correction and transmit with full power.
arXiv Detail & Related papers (2020-08-03T16:29:52Z) - A Compressive Sensing Approach for Federated Learning over Massive MIMO
Communication Systems [82.2513703281725]
Federated learning is a privacy-preserving approach to train a global model at a central server by collaborating with wireless devices.
We present a compressive sensing approach for federated learning over massive multiple-input multiple-output communication systems.
arXiv Detail & Related papers (2020-03-18T05:56:27Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.