Non-IID data in Federated Learning: A Systematic Review with Taxonomy, Metrics, Methods, Frameworks and Future Directions

Recent advances in machine learning have highlighted Federated Learning (FL) as a promising approach that enables multiple distributed users (so-called clients) to collectively train ML models without sharing their private data. While this privacy-preserving method shows potential, it struggles when data across clients is not independent and identically distributed (non-IID) data. The latter remains an unsolved challenge that can result in poorer model performance and slower training times. Despite the significance of non-IID data in FL, there is a lack of consensus among researchers about its classification and quantification. This systematic review aims to fill that gap by providing a detailed taxonomy for non-IID data, partition protocols, and metrics to quantify data heterogeneity. Additionally, we describe popular solutions to address non-IID data and standardized frameworks employed in FL with heterogeneous data. Based on our state-of-the-art review, we present key lessons learned and suggest promising future research directions.

Kinematics and Dynamic Modeling of a Planar Hydraulic Elastomer Actuator

This paper presents modeling of a compliant 2D manipulator, a so called soft hydraulic/fluidic elastomer actuator. Our focus is on fiber-Reinforced Fluidic Elastomer Actuators (RFEA) driven by a constant pressure hydraulic supply and modulated on/off valves. We present a model that not only provides the dynamics behavior of the system but also the kinematics of the actuator. In addition to that, the relation between the applied hydraulic pressure and the bending angle of the soft actuator and thus, its tip position is formulated in a systematic way. We also present a steady state model that calculates the bending angle given the fluid pressure which can be beneficial to find out the initial values of the parameters during the system identification process. Our experimental results verify and validate the performance of the proposed modeling approach both in transition and steady states. Due to its inherent simplicity, this model shall also be used in real-time control of the soft actuators.

Design, Fabrication and Control of an Hydraulic Elastomer Actuator

This paper presents design, fabrication and control of a compliant 2D manipulator, a so called soft actuator. Our focus is on fiber-reinforced elastomer actuators driven by a constant pressure hydraulic supply and modulated on/off valves. For a given diameters, we study the effect of four different elastomer materials and that of number of reinforcement fiber turns on forces generated by the actuator and maximum bending angles. For the rest of the study, we use polydimethylosiloxane (PDMS) with 240 fiber turns per 170mm length of actuator which withstand highest pressures and forces in our experiments. For the rest of the paper, we introduce two control methodologies. Firstly, we show that is possible to reasonably accurately control the pressure inside tube without measuring the pressure incorporating a simple linear tube model. This can be used, for example, in an inner-outer loop configuration with a PI position control to achieve high performance without the need for pressure measurement. Secondly, we experimentally show that a switching position control exhibits very good steady state accuracy and acceptable transient. Actuator tip position is measured using an external vision system. Our experiments included performance analysis of our soft manipulator while freely moving as well as when carrying a load.