How to send a real number using a single bit (and some shared randomness)
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Abstract (arXiv)
We consider the fundamental problem of communicating an estimate of a real number x∈[0,1] using a single bit. A sender that knows x chooses a value X∈set0,1 to transmit. In turn, a receiver estimates x based on the value of X. We consider both the biased and unbiased estimation problems and aim to minimize the cost. For the biased case, the cost is the worst-case (over the choice of x) expected squared error, which coincides with the variance if the algorithm is required to be unbiased. We first overview common biased and unbiased estimation approaches and prove their optimality when no shared randomness is allowed. We then show how a small amount of shared randomness, which can be as low as a single bit, reduces the cost in both cases. Specifically, we derive lower bounds on the cost attainable by any algorithm with unrestricted use of shared randomness and propose near-optimal solutions that use a small number of shared random bits. Finally, we discuss open problems and future directions.
Key facts. Cited by 12 papers in the map (data through 7 Oct 2026). First posted on arXiv 5 Oct 2020.
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Citing papers per year: 2021: 2; 2022: 2; 2023: 1; 2024: 2; 2025: 0; 2026: 5.
12 papers.
- 2021EDEN: Communication-Efficient and Robust Distributed Mean Estimation for Federated Learningcited by 53
- 2021DRIVE: One-bit Distributed Mean Estimationcited by 52
- 2023THC: Accelerating Distributed Deep Learning Using Tensor Homomorphic Compressioncited by 15
- 2022ScionFL: Efficient and Robust Secure Quantized Aggregationcited by 8
- 2022QUIC-FL: Quick Unbiased Compression for Federated Learningcited by 7
- 2024Beyond Throughput and Compression Ratios: Towards High End-to-end Utility of Gradient Compressioncited by 6
- 2026DynamiQ: Accelerating Gradient Synchronization using Compressed Multi-hop All-reducecited by 4
- 2026Quantizing With Randomized Hadamard Transforms: Efficient Heuristic Now Provencited by 4
- 2024Optimal and Near-Optimal Adaptive Vector Quantizationcited by 2
- 2026Entropy-Constrained Adaptive Stochastic Quantizationcited by 1
- 2026Exact Minimax One-Bit Unbiased Compression: Heavy-Tail Necessity and Finite-Randomness Approximationcited by 0
- 2026TORQUE: Optimizing What (not) to Quantize Before and After Rotationcited by 0
- 2026TORQUE: Optimizing What (not) to Quantize Before and After Rotation28 Sep 2026
- 2026Exact Minimax One-Bit Unbiased Compression: Heavy-Tail Necessity and Finite-Randomness Approximation20 Aug 2026
- 2026Entropy-Constrained Adaptive Stochastic Quantization10 Aug 2026
- 2026Quantizing With Randomized Hadamard Transforms: Efficient Heuristic Now Proven7 May 2026
- 2026DynamiQ: Accelerating Gradient Synchronization using Compressed Multi-hop All-reduce9 Feb 2026
- 2024Beyond Throughput and Compression Ratios: Towards High End-to-end Utility of Gradient Compression1 Jul 2024
- 2024Optimal and Near-Optimal Adaptive Vector Quantization5 Feb 2024
- 2023THC: Accelerating Distributed Deep Learning Using Tensor Homomorphic Compression16 Feb 2023
- 2022ScionFL: Efficient and Robust Secure Quantized Aggregation13 Oct 2022
- 2022QUIC-FL: Quick Unbiased Compression for Federated Learning26 May 2022
- 2021EDEN: Communication-Efficient and Robust Distributed Mean Estimation for Federated Learning19 Aug 2021
- 2021DRIVE: One-bit Distributed Mean Estimation18 May 2021