A Generalized Look at Federated Learning: Survey and Perspectives

Federated learning (FL) refers to a distributed machine learning framework involving learning from several decentralized edge clients without sharing local dataset. This distributed strategy prevents data leakage and enables on-device training as it updates the global model based on the local model updates. Despite offering several advantages, including data privacy and scalability, FL poses challenges such as statistical and system heterogeneity of data in federated networks, communication bottlenecks, privacy and security issues. This survey contains a systematic summarization of previous work, studies, and experiments on FL and presents a list of possibilities for FL across a range of applications and use cases. Other than that, various challenges of implementing FL and promising directions revolving around the corresponding challenges are provided.Comment: 9 pages, 2 figure

A dual source fed eleven level switched capacitor multilevel inverter with voltage boosting capability

This work introduces an 11-level switched-capacitor multilevel inverter (SCMLI) designed for solar photo-voltaic (PV) applications, capitalizing on the growing popularity of multilevel inverters due to their superior power quality. With a 1.67-times boosting capability, the proposed SCMLI employs 10 switches, 2 DC supplies, and 2 capacitors to achieve an 11-level output voltage waveform. The topology requires only seven driver circuits, incorporating 2 bidirectional switches and 3 complementary pairs of switches. The proposed inverter has intrinsic capacitor self-balancing features since the capacitors are connected across the DC voltage source at different times throughout a basic cycle to charge the capacitors at a level of input voltage. A thorough comparison between the topology and recently developed SCMLI’s has been presented. The comparison demonstrates the effectiveness in terms of switches, capacitors, sources, efficiency, total standing voltage (TSV), and boosting capacity. To experimentally validate its performance, the suggested SCMLI undergoes testing using a frequency-based switching method. The topology exhibits low total harmonic distortion (THD) of 7.65% in its output voltage waveform and 0.89% in the output current waveform

Fairness and Privacy-Preserving in Federated Learning: A Survey

Federated learning (FL) as distributed machine learning has gained popularity as privacy-aware Machine Learning (ML) systems have emerged as a technique that prevents privacy leakage by building a global model and by conducting individualized training of decentralized edge clients on their own private data. The existing works, however, employ privacy mechanisms such as Secure Multiparty Computing (SMC), Differential Privacy (DP), etc. Which are immensely susceptible to interference, massive computational overhead, low accuracy, etc. With the increasingly broad deployment of FL systems, it is challenging to ensure fairness and maintain active client participation in FL systems. Very few works ensure reasonably satisfactory performances for the numerous diverse clients and fail to prevent potential bias against particular demographics in FL systems. The current efforts fail to strike a compromise between privacy, fairness, and model performance in FL systems and are vulnerable to a number of additional problems. In this paper, we provide a comprehensive survey stating the basic concepts of FL, the existing privacy challenges, techniques, and relevant works concerning privacy in FL. We also provide an extensive overview of the increasing fairness challenges, existing fairness notions, and the limited works that attempt both privacy and fairness in FL. By comprehensively describing the existing FL systems, we present the potential future directions pertaining to the challenges of privacy-preserving and fairness-aware FL systems.Comment: 23 pages; 2 figure

Atomic Orbital Search Algorithm for Efficient Maximum Power Point Tracking in Partially Shaded Solar PV Systems

The efficient extraction of solar PV power is crucial to maximize utilization, even in rapidly changing environmental conditions. The increasing energy demands highlight the importance of solar photovoltaic (PV) systems for cost-effective energy production. However, traditional PV systems with bypass diodes at their output terminals often produce multiple power peaks, leading to significant power losses if the optimal combination of voltage and current is not achieved. To address this issue, algorithms capable of finding the highest value of a function are employed. Since the PV power output is a complex function with multiple local maximum power points (LMPPs), conventional algorithms struggle to handle partial shading conditions (PSC). As a result, nature-inspired algorithms, also known as metaheuristic algorithms, are used to maximize the power output of solar PV arrays. In this study, we introduced a novel metaheuristic algorithm called atomic orbital search for maximum power point tracking (MPPT) under PSC. The primary motivation behind this research is to enhance the efficiency and effectiveness of MPPT techniques in challenging scenarios. The proposed algorithm offers several advantages, including higher efficiency, shorter tracking time, reduced output variations, and improved duty ratios, resulting in faster convergence to the maximum power point (MPP). To evaluate the algorithm’s performance, we conducted extensive experiments using Typhoon HIL and compared it with other existing algorithms commonly employed for MPPT. The results clearly demonstrated that the proposed atomic orbital search algorithm outperformed the alternatives in terms of rapid convergence and efficient MPP tracking, particularly for complex shading patterns. This makes it a suitable choice for developing an MPP tracker applicable in various settings, such as industrial, commercial, and residential applications. In conclusion, our research addresses the pressing need for effective MPPT methods in solar PV systems operating under challenging conditions. The atomic orbital search algorithm showcases its potential in significantly improving the efficiency and performance of MPPT, ultimately contributing to the optimization of solar energy extraction and utilization