Sharing, Licensing, Buying, Selling and Operationalizing ML Models: A Deep Learning based Co-operative and Co-ordinated Security usecase

How cool is it to be able to share, buy, sell AI models and microservices to co-operate on problems that required to be solved using these AI models while still maintaining privacy?

Find our paper here: https://ieeexplore.ieee.org/abstract/document/10368564

Abstract:  Many problems that utilize Machine Learning in fact require multiple cooperative efforts to train models on different data on different targets so as to then be able to utilize all these results or the models themselves to do certain tasks. Problems in Remote sensing, Cybersecurity, Network analytics lend themselves very well to this co-operative paradigm where models built elsewhere on other data can be of direct and immediate use or at least be retrained and used at a different site. These models could in fact also come from some other organization or company (model supplier) who might charge (monetize) the usage of these models to another organization or company (model consumer) who may want to deploy them for their own use. Model sharing becomes all the more important in scenarios in which the data is subject to data sovereignty and data location requirements and, requirements over data transfer like GDPR, CCPA, etc. This sharing, monetizing, licensing, writing terms of sale of the models can be done in a very easy and streamlined fashion using the Acumos Federation and Acumos Licensing components that we have built in Acumos. To demonstrate their usage, we build an ML model to classify Distributed Denial of Service (DDoS) attacks and then set up N-site wide federation across ‘N’ Acumos instances (N=3 in the paper for demo purposes, which could represent 3 companies or 3 different physical sites) to share and use the models built elsewhere to classify DDoS attack types. This paper shows how we can set up a coordinated and cooperative defense against a cooperative attack like DDoS, and more generally solve a variety of problems across domains using ML in a cooperative fashion with Acumos.

Citation:

D. Panchal, D. Musgrove, I. Baran and D. Lu, "Sharing, Licensing, Buying, Selling and Operationalizing ML Models: A Deep Learning based Co-operative and Co-ordinated Security usecase," 2023 33rd International Telecommunication Networks and Applications Conference, Melbourne, Australia, 2023, pp. 118-123, doi: 10.1109/ITNAC59571.2023.10368564. keywords: {Training;Computational modeling;Companies;Writing;Denial-of-service attack;Data transfer;Data models;ML Model Sharing;Machine Learning;Deep Learning;GDPR;CCPA;Data Location;ML Model monetization;ML model licensing;ML model federation;Cooperative ML;Cooperative Security;DDoS;Open Source;AI4EU;Federated Learning},

Author Bio:

Deven Panchal is an AI Leader currently with AT&T Labs Research, USA. He also serves as the Chief Innovation and IP/Patents Officer of the AT&T Innovation Network and is a Senior Member of the IEEE.

He has led the launch of 4 products into Open Source, being used worldwide today. He has delivered products and platforms projected to save ~1 billion$, enable Network Services creation, Network Management, Service Management and Orchestration and to Improve Customer service. He has helped build the world’s first AI/ML marketplace, was involved with AT&T’s first 5G VRAN/CRAN trial and delivered solutions that today power AT&T’s network as well as other telco networks. His work has benefited millions of people around the world, received multiple awards and has been featured on Forbes, TechCrunch, Light Reading, TelecomTV etc.

In the past, he has served as a Research Scientist at SAMEER, IIT Bombay building India's first indigenous LINAC and MLC for Cancer Radiotherapy.

Deven Panchal has been the Owner for several subprojects within Acumos, and has led global teams in the Design and Development of these components. He has written several papers on Acumos, and has contributed heavily to the official Acumos documentation. Additionally, he has been a Top Code Contributor and Peer reviewer for Acumos.

Feel free to reach out to him or use/cite his work.