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UID:submissions.pasc-conference.org_PASC23_sess156_msa236@linklings.com
SUMMARY:Swarm Learning ~ Distributed ML for Edge Adaptive Computing
DESCRIPTION:Minisymposium\n\nBill Burnham (HPE)\n\nMachine learning (ML) h
 as enjoyed success in applications like sensor detection and object identi
 fication . In a centralized ML approach, the training data is aggregated i
 n a centralized location, where machine learning models are developed, tra
 ined, and tested. However, the centralized approach is facing mounting tec
 hnical and socioeconomic challenges. Data sovereignty, security, and priva
 cy can all create barriers to transferring and aggregating the vast amount
  of data required to train ML models. In addition, the costs and carbon im
 pact of moving massive amounts of data can be prohibitive. The industry is
  calling for an alternative. The alternative should adapt to, and take adv
 antage of, the increasingly distributed nature of data. It should achieve 
 comparable accuracy to centralized learning but outperform centralized lea
 rning in terms of security, fault-tolerance, and latency. Swarm Learning i
 s a decentralized ML solution utilizing computing power near the distribut
 ed data sources with the proven security of blockchain. In Swarm Learning,
  both training of the model and inferencing with the trained model occur a
 t edge, where data is fresh, and prompt data-driven decisions can be made.
  Only learned insights instead of the raw data are shared among collaborat
 ing ML peers, which tremendously enhances data security and privacy.\n\nDo
 main: Computer Science, Machine Learning, and Applied Mathematics 
\n\nSes
 sion Chair: Jibonananda Sanyal (National Renewable Energy Laboratory)
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