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SSTQ cuts communication costs in federated learning while ensuring privacy, achieving optimal mean squared error scaling with minimal bit usage.
No randomized polynomial-time algorithm can overcome the condition-number barrier in sparse least-squares optimization, confirming a long-standing conjecture.
