# Pareto-Optimal Bit Allocation for Collaborative Intelligence

arXiv: https://arxiv.org/abs/2009.12430 · HTML: https://mlsanity.com/paper/2009.12430/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Sep 2020.

## Built on by (most cited first, 17 of 17)

- [SC2 Benchmark: Supervised Compression for Split Computing](https://mlsanity.com/paper/2203.08875/) (2022)
- [Edge-Cloud Collaborative Computing on Distributed Intelligence and Model Optimization: A Survey](https://mlsanity.com/paper/2505.01821/) (2025)
- [Video Coding for Machine: Compact Visual Representation Compression for Intelligent Collaborative Analytics](https://mlsanity.com/paper/2110.09241/) (2021)
- [Semantically Video Coding: Instill Static-Dynamic Clues into Structured Bitstream for AI Tasks](https://mlsanity.com/paper/2201.10162/) (2022)
- [Semantic Edge Computing and Semantic Communications in 6G Networks: A Unifying Survey and Research Challenges](https://mlsanity.com/paper/2411.18199/) (2024)
- [Diff-ICMH: Harmonizing Machine and Human Vision in Image Compression with Generative Prior](https://mlsanity.com/paper/2511.22549/) (2025)
- [Compressed Feature Quality Assessment: Dataset and Baselines](https://mlsanity.com/paper/2506.07412/) (2025)
- [Just Noticeable Difference Modeling for Deep Visual Features](https://mlsanity.com/paper/2601.21933/) (2026)
- [Adversarial Attacks to Latent Representations of Distributed Neural Networks in Split Computing](https://mlsanity.com/paper/2309.17401/) (2023)
- [Model-Aware Rate-Distortion Limits for Task-Oriented Source Coding](https://mlsanity.com/paper/2602.12866/) (2026)
- [License Plate Privacy in Collaborative Visual Analysis of Traffic Scenes](https://mlsanity.com/paper/2205.01724/) (2022)
- [Resilience of Entropy Model in Distributed Neural Networks](https://mlsanity.com/paper/2403.00942/) (2024)
- [Coding for Intelligence from the Perspective of Category](https://mlsanity.com/paper/2407.01017/) (2024)
- [Analysis of Latent-Space Motion for Collaborative Intelligence](https://mlsanity.com/paper/2102.04018/) (2021)
- [DFTS2: Simulating Deep Feature Transmission Over Packet Loss Channels](https://mlsanity.com/paper/2112.00794/) (2021)
- [ACE: Towards Application-Centric Edge-Cloud Collaborative Intelligence](https://mlsanity.com/paper/2203.13061/) (2022)
- [Adaptive Transform Coding for Semantic Compression](https://mlsanity.com/paper/2604.26492/) (2026)

## Abstract (arXiv)

In recent studies, collaborative intelligence (CI) has emerged as a promising framework for deployment of Artificial Intelligence (AI)-based services on mobile/edge devices. In CI, the AI model (a deep neural network) is split between the edge and the cloud, and intermediate features are sent from the edge sub-model to the cloud sub-model. In this paper, we study bit allocation for feature coding in multi-stream CI systems. We model task distortion as a function of rate using convex surfaces similar to those found in distortion-rate theory. Using such models, we are able to provide closed-form bit allocation solutions for single-task systems and scalarized multi-task systems. Moreover, we provide analytical characterization of the full Pareto set for 2-stream k-task systems, and bounds on the Pareto set for 3-stream 2-task systems. Analytical results are examined on a variety of DNN models from the literature to demonstrate wide applicability of the results