arXiv:2605.30336v1 Announce Type: new Abstract: Federated learning is an emerging distributed paradigm that addresses the challenges posed by heterogeneous, privacy-sensitive data. It enables multiple clients to train a model collaboratively by aggregating their local updates at a server. However, conventional aggregation schemes typically use fixed weights that fail to reflect unequal and time-varying client contributions, leading to biased and unstable learning. To improve fairness and stability, we propose the Trajectory Shapley Value (TSV), a contribution metric that evaluates how each cli
The increasing prevalence of federated learning in privacy-sensitive applications necessitates robust solutions for fair and stable model training, which this research directly addresses.
This development improves the reliability and trustworthiness of federated learning, making it more viable for critical applications where equitable contributions and outcomes are paramount.
The proposed Trajectory Shapley Value offers a dynamic and fair mechanism for aggregating client contributions in federated learning, moving beyond traditional fixed-weight schemes.
- · Organizations using federated learning
- · Data privacy-focused sectors
- · AI fairness researchers
- · Healthcare and finance industries
- · Conventional federated learning aggregation methods
- · Systems with biased client contribution models
Federated learning models will become more stable and produce fairer outcomes across diverse client contributions.
Increased adoption of federated learning in highly regulated and privacy-sensitive industries due to improved fairness and stability guarantees.
The development of new regulatory frameworks or industry standards for fair AI, potentially incorporating principles similar to Trajectory Shapley Value.
This signal links to a primary source. Continuum Brief monitors and indexes it as part of the live intelligence stream — we do not republish source content.
Read at arXiv cs.LG