LSU ECE Seminar Announcement and Schedule

         On Thursday, 25 April 2024, 10:00 - 11:00,                    
         in Room 3107 Patrick F. Taylor Hall:                          
                                                                       
          Distributed Machine Learning for Intelligent Edge Computing  
                                    Systems                            
                                                                       
                               Jyotikrishna Dass                       
                                Rice University                        
                                                                       
         Abstract--With the rapid increase in data collected from      
         various edge devices across distributed networks, there is a  
         pressing need for innovative solutions to harness intelligence
         at the network edge. Traditional cloud-based centralized      
         learning methods won't suffice. Instead, federated learning,  
         an emerging approach, keeps data local at its source, avoiding
         the need for centralization on a cloud server. This method    
         pushes model updates to the edge and aggregates local updates 
         to train a global model on a shared parameter server. However,
         federated learning presents challenges, including poor model  
         convergence compared to centralized learning and device lag   
         due to heterogeneity and network unreliability. To empower    
         distributed edge intelligence efficiently, optimizing machine 
         learning models to utilize decentralized data, adapting to    
         diverse device capabilities, and complying with network       
         constraints are crucial. In this talk, I will delve deeper    
         into these insights and share my research on bridging the gap 
         between centralized and federated learning. My strategy       
         encompasses three key areas: (i) enhancing processor          
         utilization through relaxed synchronization and tackling      
         memory-efficient problems in distributed networks, (ii)       
         developing parallel algorithms that accelerate model learning 
         via data summaries and facilitate linear scaling in           
         decentralized machine learning, and (iii) co-designing        
         energy-efficient systems that make AI accessible at the edge, 
         promoting green AI. To wrap up my talk, I will share some     
         intriguing directions for future research.                    
                                                                       
         Thursday, 25 April 2024, 10:00 - 11:00                        
         Room 3107 Patrick F. Taylor Hall                              
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Schedule as of 24 Apr 2024.  For updates visit https://www.lsu.edu/eng/ece/seminar

No other future seminars currently scheduled.

For the latest schedule, abstracts of upcoming talks, and
PDF announcements suitable for posting visit

https://www.lsu.edu/eng/ece/seminar