Personalized Federated Learning for Heterogeneous Clients with Clustered Knowledge Transfer

Personalized federated learning (FL) aims to train model(s) that can perform well for individual clients that are highly data and system heterogeneous. Most work in personalized FL, however, assumes using the same model architecture at all clients and increases the communication cost by sending/receiving models. This may not be feasible for realistic scenarios of FL. In practice, clients have highly heterogeneous system-capabilities and limited communication resources. In our work, we propose a personalized FL framework, PerFed-CKT, where clients can use heterogeneous model architectures and do not directly communicate their model parameters. PerFed-CKT uses clustered co-distillation, where clients use logits to transfer their knowledge to other clients that have similar data-distributions. We theoretically show the convergence and generalization properties of PerFed-CKT and empirically show that PerFed-CKT achieves high test accuracy with several orders of magnitude lower communication cost compared to the state-of-the-art personalized FL schemes.

Client Selection in Federated Learning: Convergence Analysis and Power-of-Choice Selection Strategies

Federated learning is a distributed optimization paradigm that enables a large number of resource-limited client nodes to cooperatively train a model without data sharing. Several works have analyzed the convergence of federated learning by accounting of data heterogeneity, communication and computation limitations, and partial client participation. However, they assume unbiased client participation, where clients are selected at random or in proportion of their data sizes. In this paper, we present the first convergence analysis of federated optimization for biased client selection strategies, and quantify how the selection bias affects convergence speed. We reveal that biasing client selection towards clients with higher local loss achieves faster error convergence. Using this insight, we propose Power-of-Choice, a communication- and computation-efficient client selection framework that can flexibly span the trade-off between convergence speed and solution bias. Our experiments demonstrate that Power-of-Choice strategies converge up to 3 $times$ faster and give $10$% higher test accuracy than the baseline random selection.

V2X Downlink Coverage Analysis with a Realistic Urban Vehicular Model

As the realization of vehicular communication such as vehicle-to-vehicle (V2V) or vehicle-to-infrastructure (V2I) is imperative for the autonomous driving cars, the understanding of realistic vehicle-to-everything (V2X) models is needed. While previous research has mostly targeted vehicular models in which vehicles are randomly distributed and the variable of carrier frequency was not considered, a more realistic analysis of the V2X model is proposed in this paper. We use a one-dimensional (1D) Poisson cluster process (PCP) to model a realistic scenario of vehicle distribution in a perpendicular cross line road urban area and compare the coverage results with the previous research that distributed vehicles randomly by Poisson Point Process (PPP). Moreover, we incorporate the effect of different carrier frequencies, mmWave and sub-6 GHz, to our analysis by altering the antenna radiation pattern accordingly. Results indicated that while the effect of clustering led to lower outage, using mmWave had even more significance in leading to lower outage. Moreover, line-of-sight (LoS) interference links are shown to be more dominant in lowering the outage than the non-line-of-sight (NLoS) links even though they are less in number. The analytical results give insight into designing and analyzing the urban V2X channels, and are verified by actual urban area three-dimensional (3D) ray-tracing simulation.

RF Lens-Embedded Antenna Array for mmWave MIMO: Design and Performance

The requirement of high data-rate in the fifth generation wireless systems (5G) calls for the ultimate utilization of the wide bandwidth in the mmWave frequency band. Researchers seeking to compensate for mmWaves high path loss and to achieve both gain and directivity have proposed that mmWave multiple-input multiple-output (MIMO) systems make use of beamforming systems. Hybrid beamforming in mmWave demonstrates promising performance in achieving high gain and directivity by using phase shifters at the analog processing block. What remains a problem, however, is the actual implementation of mmWave beamforming systems; to fabricate such a system is costly and complex. With the aim of reducing such cost and complexity, this article presents actual prototypes of the lens antenna as an effective device to be used in the future 5G mmWave hybrid beamforming systems. Using a lens as a passive phase shifter enables beamforming without the heavy network of active phase shifters, while gain and directivity are achieved by the energy-focusing property of the lens. Proposed in this article are two types of lens antennas, one for static and the other for mobile usage. Their performance is evaluated using measurements and simulation data along with link-level analysis via a software defined radio (SDR) platform. Results show the promising potential of the lens antenna for its high gain and directivity, and its improved beam-switching feasibility compared to when a lens is not used. System-level evaluations reveal the significant throughput enhancement in both real indoor and outdoor environments. Moreover, the lens antennas design issues are also discussed by evaluating different lens sizes.