mlsanityData to 7 Oct 2026

Aequa: Fair Model Rewards in Collaborative Learning via Slimmable Networks

arXiv 2502.04850 ↗First posted 7 Feb 2025cs.LG cs.DCcited by 6

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Abstract (arXiv)

Collaborative learning enables multiple participants to learn a single global model by exchanging focused updates instead of sharing data. One of the core challenges in collaborative learning is ensuring that participants are rewarded fairly for their contributions, which entails two key sub-problems: contribution assessment and reward allocation. This work focuses on fair reward allocation, where the participants are incentivized through model rewards - differentiated final models whose performance is commensurate with the contribution. In this work, we leverage the concept of slimmable neural networks to collaboratively learn a shared global model whose performance degrades gracefully with a reduction in model width. We also propose a post-training fair allocation algorithm that determines the model width for each participant based on their contributions. We theoretically study the convergence of our proposed approach and empirically validate it using extensive experiments on different datasets and architectures. We also extend our approach to enable training-time model reward allocation.

Key facts. Cited by 6 papers in the map (data through 7 Oct 2026). Builds on 44 of its 61 references that are in the map. First posted on arXiv 7 Feb 2025.

Builds on

44 of 61 references.

References · 44

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32 more
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Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 1; 2026: 5.

6 papers.

  1. 2025Stochastic Self-Organization in Multi-Agent Systemscited by 9
  2. 2026MoSE: Mixture of Slimmable Experts for Efficient and Adaptive Language Modelscited by 3
  3. 2026Response-Conditioned Parallel-to-Sequential Orchestration for Multi-Agent Systemscited by 1
  4. 2026Data-Free Contribution Estimation in Federated Learning using Gradient von Neumann Entropycited by 0
  5. 2026Class-wise Contribution Estimation via Logit Maximization for Federated Learningcited by 0
  6. 2026PreLort: Prefix-Nested LoRA for Federated Fine-Tuning under Rank Heterogeneitycited by 0