Defense + Commercial Sensing / 2019

Model poisoning attacks against distributed machine learning systems

Richard J. Tomsett, K. Chan, Supriyo Chakraborty

Foundation ModelsLarge Language ModelsML SystemsPopular and Landmark Papers

Future military coalition operations will increasingly rely on machine learning (ML) methods to improve situational awareness. The coalition context presents unique challenges for ML: the tactical environment creates significant computing and communications limitations while also having to deal with an adversarial presence. Further, coalition operations must operate in a distributed manner, while coping with the constraints posed by the operational environment. Envisioned ML deployments in military assets must be resilient to these challenges. Here, we focus on the susceptibility of ML models to be poisoned (during training) or fooled (after training) by adversarial inputs. We review recent work on distributed adversarial ML, and present new results from our own investigations into model poisoning attacks on distributed learning systems without a central parameter aggregation node.

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### Paper attached as context
Title: Model poisoning attacks against distributed machine learning systems
Authors: Richard J. Tomsett, K. Chan, Supriyo Chakraborty
Year: 2019
Venue: Defense + Commercial Sensing
Categories: Foundation Models, Large Language Models, ML Systems, Popular and Landmark Papers
Citations: 35
Paper URL: https://www.semanticscholar.org/paper/4487c0c70abfa2b9ce0c5570847fbf0386185125
Open PDF: Not available

Abstract:
Future military coalition operations will increasingly rely on machine learning (ML) methods to improve situational awareness. The coalition context presents unique challenges for ML: the tactical environment creates significant computing and communications limitations while also having to deal with an adversarial presence. Further, coalition operations must operate in a distributed manner, while coping with the constraints posed by the operational environment. Envisioned ML deployments in military assets must be resilient to these challenges. Here, we focus on the susceptibility of ML models to be poisoned (during training) or fooled (after training) by adversarial inputs. We review recent work on distributed adversarial ML, and present new results from our own investigations into model poisoning attacks on distributed learning systems without a central parameter aggregation node.

Learning resources:
- Google Scholar: Google Scholar references (https://scholar.google.com/scholar?q=Model%20poisoning%20attacks%20against%20distributed%20machine%20learning%20systems)
- Papers with Code: Papers with Code search (https://paperswithcode.com/search?q=Model%20poisoning%20attacks%20against%20distributed%20machine%20learning%20systems)
- Semantic Scholar: Semantic Scholar paper page (https://www.semanticscholar.org/paper/4487c0c70abfa2b9ce0c5570847fbf0386185125)
- YouTube: YouTube explanations (https://www.youtube.com/results?search_query=Model%20poisoning%20attacks%20against%20distributed%20machine%20learning%20systems+paper+explained)