# Fair Resource Allocation in Multi-Task Learning

arXiv: https://arxiv.org/abs/2402.15638 · HTML: https://mlsanity.com/paper/2402.15638/

## Key facts

- Cited by 35 papers in the map (data through 14 Oct 2026).
- Builds on 36 of its 67 references that are in the map.
- First posted on arXiv 23 Feb 2024.

## Builds on (most cited first, 30 of 36)

- [Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor](https://mlsanity.com/paper/1801.01290/) (2018)
- [The Cityscapes Dataset for Semantic Urban Scene Understanding](https://mlsanity.com/paper/1604.01685/) (2016)
- [Deep Learning Face Attributes in the Wild](https://mlsanity.com/paper/1411.7766/) (2014)
- [Fast Graph Representation Learning with PyTorch Geometric](https://mlsanity.com/paper/1903.02428/) (2019)
- [SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation](https://mlsanity.com/paper/1511.00561/) (2015)
- [Multi-Task Learning Using Uncertainty to Weigh Losses for Scene Geometry and Semantics](https://mlsanity.com/paper/1705.07115/) (2017)
- [BDD100K: A Diverse Driving Dataset for Heterogeneous Multitask Learning](https://mlsanity.com/paper/1805.04687/) (2018)
- [Gradient Surgery for Multi-Task Learning](https://mlsanity.com/paper/2001.06782/) (2020)
- [Meta-World: A Benchmark and Evaluation for Multi-Task and Meta Reinforcement Learning](https://mlsanity.com/paper/1910.10897/) (2019)
- [A Survey on Multi-Task Learning](https://mlsanity.com/paper/1707.08114/) (2017)
- [GradNorm: Gradient Normalization for Adaptive Loss Balancing in Deep Multitask Networks](https://mlsanity.com/paper/1711.02257/) (2017)
- [Multi-Task Learning as Multi-Objective Optimization](https://mlsanity.com/paper/1810.04650/) (2018)
- [An Overview of Multi-Task Learning in Deep Neural Networks](https://mlsanity.com/paper/1706.05098/) (2017)
- [End-to-End Multi-Task Learning with Attention](https://mlsanity.com/paper/1803.10704/) (2018)
- [Conflict-Averse Gradient Descent for Multi-task Learning](https://mlsanity.com/paper/2110.14048/) (2021)
- [Fair Resource Allocation in Federated Learning](https://mlsanity.com/paper/1905.10497/) (2019)
- [Multi-Task Learning as a Bargaining Game](https://mlsanity.com/paper/2202.01017/) (2022)
- [Just Pick a Sign: Optimizing Deep Multitask Models with Gradient Sign Dropout](https://mlsanity.com/paper/2010.06808/) (2020)
- [UberNet: Training a `Universal' Convolutional Neural Network for Low-, Mid-, and High-Level Vision using Diverse Datasets and Limited Memory](https://mlsanity.com/paper/1609.02132/) (2016)
- [Gradient Vaccine: Investigating and Improving Multi-task Optimization in Massively Multilingual Models](https://mlsanity.com/paper/2010.05874/) (2020)
- [Recurrent Neural Network for Text Classification with Multi-Task Learning](https://mlsanity.com/paper/1605.05101/) (2016)
- [Instance-aware Semantic Segmentation via Multi-task Network Cascades](https://mlsanity.com/paper/1512.04412/) (2015)
- [Reasonable Effectiveness of Random Weighting: A Litmus Test for Multi-Task Learning](https://mlsanity.com/paper/2111.10603/) (2021)
- [FAMO: Fast Adaptive Multitask Optimization](https://mlsanity.com/paper/2306.03792/) (2023)
- [Revisiting Scalarization in Multi-Task Learning: A Theoretical Perspective](https://mlsanity.com/paper/2308.13985/) (2023)
- [MTL-NAS: Task-Agnostic Neural Architecture Search towards General-Purpose Multi-Task Learning](https://mlsanity.com/paper/2003.14058/) (2020)
- [Multi-task Learning with Attention for End-to-end Autonomous Driving](https://mlsanity.com/paper/2104.10753/) (2021)
- [A Survey of Multi-task Learning in Natural Language Processing: Regarding Task Relatedness and Training Methods](https://mlsanity.com/paper/2204.03508/) (2022)
- [Three-Way Trade-Off in Multi-Objective Learning: Optimization, Generalization and Conflict-Avoidance](https://mlsanity.com/paper/2305.20057/) (2023)
- [Direction-oriented Multi-objective Learning: Simple and Provable Stochastic Algorithms](https://mlsanity.com/paper/2305.18409/) (2023)

## Built on by (most cited first, 30 of 35)

- [Gradient-Based Multi-Objective Deep Learning: Algorithms, Theories, Applications, and Beyond](https://mlsanity.com/paper/2501.10945/) (2025)
- [MT2ST: Adaptive Multi-Task to Single-Task Learning](https://mlsanity.com/paper/2406.18038/) (2024)
- [U-Fair: Uncertainty-based Multimodal Multitask Learning for Fairer Depression Detection](https://mlsanity.com/paper/2501.09687/) (2025)
- [MTL-UE: Learning to Learn Nothing for Multi-Task Learning](https://mlsanity.com/paper/2505.05279/) (2025)
- [PiKE: Adaptive Data Mixing for Large-Scale Multi-Task Learning Under Low Gradient Conflicts](https://mlsanity.com/paper/2502.06244/) (2025)
- [MGDA Converges under Generalized Smoothness, Provably](https://mlsanity.com/paper/2405.19440/) (2024)
- [LDC-MTL: Balancing Multi-Task Learning through Scalable Loss Discrepancy Control](https://mlsanity.com/paper/2502.08585/) (2025)
- [FairHuman: Boosting Hand and Face Quality in Human Image Generation with Minimum Potential Delay Fairness in Diffusion Models](https://mlsanity.com/paper/2507.02714/) (2025)
- [Continual Optimization with Symmetry Teleportation for Multi-Task Learning](https://mlsanity.com/paper/2503.04046/) (2025)
- [ThanoRA: Task Heterogeneity-Aware Multi-Task Low-Rank Adaptation](https://mlsanity.com/paper/2505.18640/) (2025)
- [Revisit the Imbalance Optimization in Multi-task Learning: An Experimental Analysis](https://mlsanity.com/paper/2509.23915/) (2025)
- [Rep-MTL: Unleashing the Power of Representation-level Task Saliency for Multi-Task Learning](https://mlsanity.com/paper/2507.21049/) (2025)
- [AutoScale: Linear Scalarization Guided by Multi-Task Optimization Metrics](https://mlsanity.com/paper/2508.13979/) (2025)
- [NTKMTL: Mitigating Task Imbalance in Multi-Task Learning from Neural Tangent Kernel Perspective](https://mlsanity.com/paper/2510.18258/) (2025)
- [Breaking the Bias Barrier in Concave Multi-Objective Reinforcement Learning](https://mlsanity.com/paper/2603.08518/) (2026)
- [Delve into the Applicability of Advanced Optimizers for Multi-Task Learning](https://mlsanity.com/paper/2604.08939/) (2026)
- [SAMO: A Lightweight Sharpness-Aware Approach for Multi-Task Optimization with Joint Global-Local Perturbation](https://mlsanity.com/paper/2507.07883/) (2025)
- [Graph Coloring for Multi-Task Learning](https://mlsanity.com/paper/2509.16959/) (2025)
- [Parameter-Efficient Multi-Task Learning via Progressive Task-Specific Adaptation](https://mlsanity.com/paper/2509.19602/) (2025)
- [Towards Relaxed Multimodal Inputs for Gait-based Parkinson's Disease Assessment](https://mlsanity.com/paper/2510.15748/) (2025)
- [Simplifying Multi-Task Architectures Through Task-Specific Normalization](https://mlsanity.com/paper/2512.20420/) (2025)
- [MAFS: Masked Autoencoder for Infrared-Visible Image Fusion and Semantic Segmentation](https://mlsanity.com/paper/2509.11817/) (2025)
- [Monotonic Transformation Invariant Multi-task Learning](https://mlsanity.com/paper/2509.23948/) (2025)
- [VAO: Validation-Aligned Optimization for Cross-Task Generative Auto-Bidding](https://mlsanity.com/paper/2510.07760/) (2025)
- [Equitable Multi-Task Learning for AI-RANs](https://mlsanity.com/paper/2603.08717/) (2026)
- [Computation and Communication Efficient Federated Unlearning via On-server Gradient Conflict Mitigation and Expression](https://mlsanity.com/paper/2603.13795/) (2026)
- [Flatness and Gradient Alignment Are Both Necessary: Spectral-Aware Gradient-Aligned Exploration for Multi-Distribution Learning](https://mlsanity.com/paper/2605.07914/) (2026)
- [TOPPO: Rethinking PPO for Multi-Task Reinforcement Learning with Critic Balancing](https://mlsanity.com/paper/2605.11473/) (2026)
- [A Unified Framework for Gradient Aggregation in Multi-Objective Optimization](https://mlsanity.com/paper/2605.30452/) (2026)
- [CORE-MTL: Rethinking Gradient Balancing via Causal Orthogonal Representations](https://mlsanity.com/paper/2606.02221/) (2026)

## Abstract (arXiv)

By jointly learning multiple tasks, multi-task learning (MTL) can leverage the shared knowledge across tasks, resulting in improved data efficiency and generalization performance. However, a major challenge in MTL lies in the presence of conflicting gradients, which can hinder the fair optimization of some tasks and subsequently impede MTL's ability to achieve better overall performance. Inspired by fair resource allocation in communication networks, we formulate the optimization of MTL as a utility maximization problem, where the loss decreases across tasks are maximized under different fairness measurements. To solve this problem, we propose FairGrad, a novel MTL optimization method. FairGrad not only enables flexible emphasis on certain tasks but also achieves a theoretical convergence guarantee. Extensive experiments demonstrate that our method can achieve state-of-the-art performance among gradient manipulation methods on a suite of multi-task benchmarks in supervised learning and reinforcement learning. Furthermore, we incorporate the idea of α-fairness into loss functions of various MTL methods. Extensive empirical studies demonstrate that their performance can be significantly enhanced. Code is provided at urlhttps://github.com/OptMN-Lab/fairgrad.