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Existing defenses against backdoor attacks in Vertical Federated Learning are fundamentally flawed, often relying on unrealistic assumptions that mask their true vulnerabilities.
LLMs can now automatically slim down and future-proof mathematical proofs, achieving 70% compression and 60% faster compilation by strategically rewriting them.
