Navigating the Future of Federated Recommendation Systems with Foundation Models
Fuente:
arXiv
Salvato in:
| Autori principali: | , , , , , |
|---|---|
| Natura: | Preprint |
| Pubblicazione: |
2024
|
| Soggetti: | |
| Accesso online: | |
| Tags: |
Aggiungi Tag
Nessun Tag, puoi essere il primo ad aggiungerne!!
|
| _version_ | 1866909574505693184 |
|---|---|
| author | Li, Zhiwei Long, Guodong Zhang, Chunxu Zhang, Honglei Jiang, Jing Zhang, Chengqi |
| author_facet | Li, Zhiwei Long, Guodong Zhang, Chunxu Zhang, Honglei Jiang, Jing Zhang, Chengqi |
| contents | Federated Recommendation Systems (FRSs) offer a privacy-preserving alternative to traditional centralized approaches by decentralizing data storage. However, they face persistent challenges such as data sparsity and heterogeneity, largely due to isolated client environments. Recent advances in Foundation Models (FMs), particularly large language models like ChatGPT, present an opportunity to surmount these issues through powerful, cross-task knowledge transfer. In this position paper, we systematically examine the convergence of FRSs and FMs, illustrating how FM-enhanced frameworks can substantially improve client-side personalization, communication efficiency, and server-side aggregation. We also delve into pivotal challenges introduced by this integration, including privacy-security trade-offs, non-IID data, and resource constraints in federated setups, and propose prospective research directions in areas such as multimodal recommendation, real-time FM adaptation, and explainable federated reasoning. By unifying FRSs with FMs, our position paper provides a forward-looking roadmap for advancing privacy-preserving, high-performance recommendation systems that fully leverage large-scale pre-trained knowledge to enhance local performance. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2406_00004 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Navigating the Future of Federated Recommendation Systems with Foundation Models Li, Zhiwei Long, Guodong Zhang, Chunxu Zhang, Honglei Jiang, Jing Zhang, Chengqi Information Retrieval Artificial Intelligence Machine Learning Federated Recommendation Systems (FRSs) offer a privacy-preserving alternative to traditional centralized approaches by decentralizing data storage. However, they face persistent challenges such as data sparsity and heterogeneity, largely due to isolated client environments. Recent advances in Foundation Models (FMs), particularly large language models like ChatGPT, present an opportunity to surmount these issues through powerful, cross-task knowledge transfer. In this position paper, we systematically examine the convergence of FRSs and FMs, illustrating how FM-enhanced frameworks can substantially improve client-side personalization, communication efficiency, and server-side aggregation. We also delve into pivotal challenges introduced by this integration, including privacy-security trade-offs, non-IID data, and resource constraints in federated setups, and propose prospective research directions in areas such as multimodal recommendation, real-time FM adaptation, and explainable federated reasoning. By unifying FRSs with FMs, our position paper provides a forward-looking roadmap for advancing privacy-preserving, high-performance recommendation systems that fully leverage large-scale pre-trained knowledge to enhance local performance. |
| title | Navigating the Future of Federated Recommendation Systems with Foundation Models |
| topic | Information Retrieval Artificial Intelligence Machine Learning |
| url | https://arxiv.org/abs/2406.00004 |