Giskard : Byzantine Robust and Confidential Aggregation for Large-Scale Decentralized Learning
arXiv:2606.19129v1 Announce Type: cross Abstract: Dealing simultaneously with confidentiality and Byzantine behaviors in decentralized learning is a challenging problem. Indeed, in decentralized learning, clients train a machine learning model while keeping their data locally and share their model parameters or gradients with a set of neighbors. While enforcing confidentiality calls for hiding the exchanged model parameters/gradients (e.g., by using cryptographic techniques), dealing with Byzantine contributions often requires inspecting the latter. Hence, most research works address these obj
The increasing adoption of decentralized learning and federated AI models necessitates robust solutions to address data confidentiality and malicious participation, which are critical for trust and widespread deployment.
This research addresses fundamental challenges in securing decentralized AI, enabling more trustworthy and privacy-preserving machine learning applications across various sensitive domains like finance, healthcare, and defense.
The development of solutions like Giskard allows organizations to leverage collaborative AI training without fully exposing proprietary data or being vulnerable to adversarial attacks, expanding the potential applications of AI.
- · Organizations using federated learning
- · Privacy-focused AI developers
- · Cybersecurity firms
- · Decentralized AI platforms
- · Malicious actors in decentralized AI
- · Systems vulnerable to data breaches
- · Centralized data aggregators
More secure and confidential decentralized AI models will accelerate adoption in sensitive industries.
Increased trust in these systems could lead to new collaborative AI applications across competing entities.
The development of robust, privacy-preserving AI could democratize powerful AI capabilities, reducing reliance on centralized data monopolies.
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Read at arXiv cs.LG