A Comparative Evaluation of FedAvg and Per-FedAvg Algorithms for Dirichlet Distributed Heterogeneous Data

In this paper, we investigate Federated Learning (FL), a paradigm of machine learning that allows for decentralized model training on devices without sharing raw data, there by preserving data privacy. In particular, we compare two strategies within this paradigm: Federated Averaging (FedAvg) and Personalized Federated Averaging (Per-FedAvg), focusing on their performance with Non-Identically and Independently Distributed (Non-IID) data. Our analysis shows that the level of data heterogeneity, modeled using a Dirichlet distribution, significantly affects the performance of both strategies, with Per-FedAvg showing superior robustness in conditions of high heterogeneity. Our results provide insights into the development of more effective and efficient machine learning strategies in a decentralized setting.Comment: 6 pages, 5 figures, conferenc

Multi-access edge computing: A survey

Multi-access Edge Computing (MEC) is a key solution that enables operators to open their networks to new services and IT ecosystems to leverage edge-cloud benefits in their networks and systems. Located in close proximity from the end users and connected devices, MEC provides extremely low latency and high bandwidth while always enabling applications to leverage cloud capabilities as necessary. In this article, we illustrate the integration of MEC into a current mobile networks' architecture as well as the transition mechanisms to migrate into a standard 5G network architecture.We also discuss SDN, NFV, SFC and network slicing as MEC enablers. Then, we provide a state-of-the-art study on the different approaches that optimize the MEC resources and its QoS parameters. In this regard, we classify these approaches based on the optimized resources and QoS parameters (i.e., processing, storage, memory, bandwidth, energy and latency). Finally, we propose an architectural framework for a MEC-NFV environment based on the standard SDN architecture

The Uplink Capacity Evaluation of Wireless Networks: Spectral Analysis Approach

In this paper we study the capacity of wireless cellular network, in particular the uplink of WCDMA system by using the two dimensional continuous-time Markov chain (CTMC) technique. Considering two types of calls: real-time (RT) calls that characterized by a quasi fixed transmission rate, and best-effort (BE) calls which do not require strict demand but need some reliability conditions. We develop an approach based on the spectral analysis for evaluating the cell capacity. We explicitly obtain the simultaneous distribution of the number of RT connections and the number of BE connections in the steady-state. This analysis allows us to simplify the computation of the performance measures including expected delay and throughput of BE traffic. These performances are obtained explicitly in both cases (finite and infinite) of BE calls as function of system parameters like arrival rate of BE and RT calls, service rate of BE and RT calls. These results allow the operator to evaluate the cell capacity by varying these parameters independently of the number of BE calls according to its policy to manage the network. Note that this analysis can be applied to various systems such as WiMAX/HSPA, and for both uplink and downlink scenarios, so our spectral analysis approach is not only applicable to the uplink of WCDMA system. We further propose some CAC (Call admission control) policies for BE traffic. We finally conclude this work by some numerical and simulation results. The simulation results obtained by the network simulator (NS2) are closely to the numerical results of our analytical results which validate our theoretical model

Evaluation des performances dans les réseaux cellulaires WCDMA avec des applications multimédia

Une évolution majeure des systèmes de troisième génération (3G) en comparaison avec ceux de deuxième génération (2G) est la possibilité d offrir des services de haut débit. Ceci permet l introduction des nouveaux services et notamment des services de données. Il est envisagé que les services de données formeront la plus grande partie du trafic écoulé dans les réseaux de 3G. Ces services vont coexister avec le service voix, déjà présent dans les systèmes de 2G. Par conséquent, des scénarios de trafic mixte doivent être considérés. L existence des différents types de trafic augmente la complexité de la gestion des ressources radios. Le développement de systèmes de communication est actuellement limité par les contraintes sur la capacité de ces systèmes. En effet, l augmentation du nombre d accès simultanés génère de l interférence qui dégrade la qualité de la communication. La définition de la capacité du réseau cellulaire WCDMA est liée au nombre d appels ou bien au taux d arrivée des appels qui garantit que la probabilité de rejet est inférieure à un seuil donné (capacité d Erlang). Dans cette thèse nous calculons explicitement la capacité de liaison montante du système WCDMA avec deux types de trafic à savoir les appels en temps réel (RT) et les appels non temps réel appelés Best Effort (BE). Nous utilisons deux approches pour étudier les performances du système à savoir la méthode d analyse spectrale et la méthode de perturbation singulière basée sur l approximation. Nous comparons les deux approches puis nous validons notre résultat par le simulateur NS2. Nous étudions l impact des pertes de paquets sur les performances du réseau cellulaire 3G en présence des applications multimédias. Par la suite, nous nous penchons sur l étude des performances d un système hétérogène composé de deux topologies à savoir le réseau UMTS et le réseau Ad-hocA major evolution of the third generation systems (3G) compared with those of second generation (2G), is the possibility to offer high and very high rates services. It allows the introduction of the new services and chiefly supports data transmissions. It s expected that data services will form the biggest part of the traffic in 3G network. These services coexist with the voice service, which is already present in 2G systems. Therefore, schemes witch mixed traffic should be considered. The existence of the different types of traffic will lead then to an increased complexity and make management of the resources radios a real issue. The development of communication systems is limited currently by the constraints on the capacity of these systems. Indeed, the increase of the number of simultaneous access generates interference that deteriorates the quality of end to end the communication. The definition of the capacity of the WCDMA cellular network is bounded by the number of simultaneous calls or the rate of arrival that guarantees a reject probability lower than some given threshold (Erlang capacity ). In this thesis we explicitly calculate the capacity of uplink case the WCDMA systems in presence of two types of traffic. Namely real time calls (RT) and non real time calls (BE). We use two approaches to study the performances of this system to know indeed, we used the spectral analysis method and the singular perturbation method based on the approximation. We then compare the two approaches and validate our results using NS2 simulator. There after we bend a survey of the performances of a heterogeneous system, composed of two different topologies a UMTS subsystem and an Ad-hoc subsystemAVIGNON-BU Centrale (840072102) / SudocSudocFranceF

Lightweight Mobile Core Networks for Machine Type Communications

International audienceMachine type communications (MTCs) enable the communications of machines (devices) to machines over mobile networks. Besides simplifying our daily lives, the MTC business represents a promising market for mobile operators to increase their revenues. However, before a complete deployment of MTC over mobile networks, there is need to update the specifications of mobile networks in order to cope with the expected high number (massive deployment) of MTC devices. Indeed, large scale deployment of MTC devices represents an important challenge as a high number of MTC devices, simultaneously connecting to the mobile network, may cause congestion and system overload, which can degrade the network performance and even result in network node failures. Several activities have been led by 3GPP to alleviate system overload introduced by MTC. Most of the devised approaches represent only incremental solutions. Unlike these solutions, we devise a complete new architectural vision to support MTC in mobile networks. This vision relies on the marriage of mobile networks and cloud computing, specifically exploiting recent advances in network function virtualization (NFV). The aim of the proposed vision, namely LightEPC, is: 1) to orchestrate the on-demand creation of cloud-based lightweight mobile core networks dedicated for MTC and 2) to simplify the network attach procedure for MTC devices by creating only one NFV MTC function that groups all the usual procedures. By doing so, LightEPC is able to create and scale instances of NFV MTC functions on demand and in an elastic manner to cope with any sudden increase in traffic generated by MTC devices. To evaluate LightEPC, some preliminary analysis were conducted and the obtained analytical results indicate the ability of LightEPC in alleviating congestion and scaling up fast with massive numbers of MTC devices in mobile networks. Finally, a real-life implementation of LightEPC on top of cloud platform is discussed

Dynamic SDN-based radio access network slicing with deep reinforcement learning for URLLC and eMBB services

Radio access network (RAN) slicing is a key technology that enables 5G network to support heterogeneous requirements of generic services, namely ultra-reliable low-latency communication (URLLC) and enhanced mobile broadband (eMBB). In this paper, we propose a two time-scales RAN slicing mechanism to optimize the performance of URLLC and eMBB services. In a large time-scale, an SDN controller allocates radio resources to gNodeBs according to the requirements of the eMBB and URLLC services. In a short time-scale, each gNodeB allocates its available resources to its end-users and requests, if needed, additional resources from adjacent gNodeBs. We formulate this problem as a non-linear binary program and prove its NP-hardness. Next, for each time-scale, we model the problem as a Markov decision process (MDP), where the large-time scale is modeled as a single agent MDP whereas the shorter time-scale is modeled as a multi-agent MDP. We leverage the exponential-weight algorithm for exploration and exploitation (EXP3) to solve the single-agent MDP of the large time-scale MDP and the multi-agent deep Q-learning (DQL) algorithm to solve the multi-agent MDP of the short time-scale resource allocation. Extensive simulations show that our approach is efficient under different network parameters configuration and it outperforms recent benchmark solutions