# Goal-Oriented Communications in Federated Learning via Feedback on Risk-Averse Participation

arXiv: https://arxiv.org/abs/2305.11633 · HTML: https://mlsanity.com/paper/2305.11633/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 22 references that are in the map.
- First posted on arXiv 19 May 2023.

## Builds on (most cited first, 14 of 14)

- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [Advances and Open Problems in Federated Learning](https://mlsanity.com/paper/1912.04977/) (2019)
- [Client Selection for Federated Learning with Heterogeneous Resources in Mobile Edge](https://mlsanity.com/paper/1804.08333/) (2018)
- [Towards Efficient Data Valuation Based on the Shapley Value](https://mlsanity.com/paper/1902.10275/) (2019)
- [Beyond Transmitting Bits: Context, Semantics, and Task-Oriented Communications](https://mlsanity.com/paper/2207.09353/) (2022)
- [6G Networks: Beyond Shannon Towards Semantic and Goal-Oriented Communications](https://mlsanity.com/paper/2011.14844/) (2020)
- [Semantics-Empowered Communication for Networked Intelligent Systems](https://mlsanity.com/paper/2007.11579/) (2020)
- [Edge Learning for B5G Networks with Distributed Signal Processing: Semantic Communication, Edge Computing, and Wireless Sensing](https://mlsanity.com/paper/2206.00422/) (2022)
- [MAB-based Client Selection for Federated Learning with Uncertain Resources in Mobile Networks](https://mlsanity.com/paper/2009.13879/) (2020)
- [FedToken: Tokenized Incentives for Data Contribution in Federated Learning](https://mlsanity.com/paper/2209.09775/) (2022)
- [A Contribution-based Device Selection Scheme in Federated Learning](https://mlsanity.com/paper/2203.05369/) (2022)
- [On the Convergence of the Shapley Value in Parametric Bayesian Learning Games](https://mlsanity.com/paper/2205.07428/) (2022)
- [Scheduling Policy for Value-of-Information (VoI) in Trajectory Estimation for Digital Twins](https://mlsanity.com/paper/2301.10962/) (2023)
- [On-board Change Detection for Resource-efficient Earth Observation with LEO Satellites](https://mlsanity.com/paper/2305.10119/) (2023)

## Built on by (most cited first, 3 of 3)

- [Greedy Shapley Client Selection for Communication-Efficient Federated Learning](https://mlsanity.com/paper/2312.09108/) (2023)
- [Aligning Task- and Reconstruction-Oriented Communications for Edge Intelligence](https://mlsanity.com/paper/2502.15472/) (2025)
- [Task-Oriented Co-Design of Communication, Computing, and Control for Edge-Enabled Industrial Cyber-Physical Systems](https://mlsanity.com/paper/2503.08661/) (2025)

## Abstract (arXiv)

We treat the problem of client selection in a Federated Learning (FL) setup, where the learning objective and the local incentives of the participants are used to formulate a goal-oriented communication problem. Specifically, we incorporate the risk-averse nature of participants and obtain a communication-efficient on-device performance, while relying on feedback from the Parameter Server (textttPS). A client has to decide its transmission plan on when not to participate in FL. This is based on its intrinsic incentive, which is the value of the trained global model upon participation by this client. Poor updates not only plunge the performance of the global model with added communication cost but also propagate the loss in performance on other participating devices. We cast the relevance of local updates as semantic information for developing local transmission strategies, i.e., making a decision on when to ``not transmit". The devices use feedback about the state of the PS and evaluate their contributions in training the learning model in each aggregation period, which eventually lowers the number of occupied connections. Simulation results validate the efficacy of our proposed approach, with up to 1.4× gain in communication links utilization as compared with the baselines.