Summary of Valid: a Validated Algorithm For Learning in Decentralized Networks with Possible Adversarial Presence, by Mayank Bakshi et al.
VALID: a Validated Algorithm for Learning in Decentralized Networks with Possible Adversarial Presence
by Mayank Bakshi, Sara Ghasvarianjahromi, Yauhen Yakimenka, Allison Beemer, Oliver Kosut, Joerg Kliewer
First submitted to arxiv on: 12 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Information Theory (cs.IT)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper introduces a new paradigm for decentralized learning on undirected networks with heterogeneous data and potential adversarial infiltration. The VALID protocol achieves a validated learning guarantee, converging to a global empirical loss minimizer when adversaries are absent or detecting their presence and adapting accordingly. Notably, VALID offers an O(1/T) convergence rate, comparable computational and communication complexities to non-adversarial distributed stochastic gradient descent, and optimal performance metrics in adversary-free environments. The protocol relies on a heterogeneity metric based on individual agents’ gradients, allowing for efficient detection of byzantine disruptions and proving the optimality of VALID in wide generality. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about making sure that when machines learn together, they can do it safely and fairly. When some machines might be trying to trick others, this new way of learning (called VALID) makes sure that everyone stays honest or warns the network if someone’s being dishonest. This method works really well, even when there are no bad actors involved, and is much faster than other methods when things do go wrong. |
Keywords
» Artificial intelligence » Stochastic gradient descent
