Wireless Federated $k$-Means Clustering with Non-coherent Over-the-Air Computation
This addresses communication efficiency for federated clustering in wireless networks, but it is incremental as it builds on existing federated and OAC methods.
The paper tackles the problem of high communication latency in wireless federated k-means clustering by proposing an over-the-air computation scheme that uses signal superposition to sum updates non-coherently, eliminating the need for precise synchronization, and it performs similarly to standard k-means while reducing latency.
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.