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"A Review of Solving Non-IID Data in Federated Learning: Current Status" by Wenhai Lu, Jieren Cheng et al.

Federated learning (FL), as a machine learning framework, has garnered substantial attention from researchers in recent years. FL makes it possible to train a global model through coordination by a central server while ensuring the privacy of data on individual edge devices. However, the data on edge devices that participate in FL training are not independently and identically distributed (IID), resulting in challenges related to heterogeneity data. In this paper, we introduce the challenges generated by non-IID data to FL and provide a detailed classification of non-IID data. Then, we summarize the existing solutions to non-IID data in FL from the perspectives of data and process. To the best of our knowledge, despite the considerable efforts achieved by many researchers in solving the non-IID problem, some issues remain unsolved. This paper provides researchers with the latest findings and analyzes the potential future directions for solving non-IID in FL. ....

Federated Learning , Heterogeneous Data , On Iid Data ,

OASIS initiative provides $1.7 million for UCR research projects

UCR's Office of Research and Economic Development (RED) has announced over $1.7 million in competitive funding for faculty research projects through its Opportunity to Advance Sustainability Innovation and Social Inclusion initiative, known as OASIS. The projects range from probing the severity of COVID-19 infection in Riverside County, through sustainability and resilience in agriculture; to finding ways to mine lithium needed for electric car batteries from brine below the Salton Sea. ....

United States , Salton Sea , Riverside County , Gregory Beran , Haizhou Liu , Kate Ostevik , Mark Wolfson , Jinyong Liu , Elia Scudiero , Qingfang Wang , Kendrick Davis , Konstantinos Karydis , Chikako Takeshita , Christopher Fichtner , Vincent Lavallo , Michael Mckibben , Amir Haghverdi , Basak Guler , Hideaki Tsutsui , Matthew Barth , Valentine Vullev , Marissa Brookes , Timothy Lyons , Hoori Ajami , Jennifer Syvertsen , Juchen Guo ,