Cooperation and Personalization on a Seesaw: Choice-based FL for Safe Cooperation in Wireless Networks
Federated learning (FL) is an innovative distributed artificial intelligence (AI) technique. It has been used for interdisciplinary studies in different fields such as healthcare, marketing and finance. However the application of FL in wireless networks is still in its infancy. In this work, we firs...
Gespeichert in:
| Hauptverfasser: | , , , , , |
|---|---|
| Format: | Artikel |
| Sprache: | eng |
| Schlagworte: | |
| Online-Zugang: | Volltext bestellen |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| container_end_page | |
|---|---|
| container_issue | |
| container_start_page | |
| container_title | |
| container_volume | |
| creator | Zhang, Han Elsayed, Medhat Bavand, Majid Gaigalas, Raimundas Ozcan, Yigit Erol-Kantarci, Melike |
| description | Federated learning (FL) is an innovative distributed artificial intelligence
(AI) technique. It has been used for interdisciplinary studies in different
fields such as healthcare, marketing and finance. However the application of FL
in wireless networks is still in its infancy. In this work, we first overview
benefits and concerns when applying FL to wireless networks. Next, we provide a
new perspective on existing personalized FL frameworks by analyzing the
relationship between cooperation and personalization in these frameworks.
Additionally, we discuss the possibility of tuning the cooperation level with a
choice-based approach. Our choice-based FL approach is a flexible and safe FL
framework that allows participants to lower the level of cooperation when they
feel unsafe or unable to benefit from the cooperation. In this way, the
choice-based FL framework aims to address the safety and fairness concerns in
FL and protect participants from malicious attacks. |
| doi_str_mv | 10.48550/arxiv.2411.04159 |
| format | Article |
| fullrecord | <record><control><sourceid>arxiv_GOX</sourceid><recordid>TN_cdi_arxiv_primary_2411_04159</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>2411_04159</sourcerecordid><originalsourceid>FETCH-arxiv_primary_2411_041593</originalsourceid><addsrcrecordid>eNqFjrEKwjAURbM4iPoBTr4faG20BXUtFgcRoYJjeNoXDNakvBSrfr20Org5XThcDkeIsYzCeJEk0RT5Ye7hLJYyjGKZLPtCp85VxFgbZwFtAXti7yyW5vVhLYacyGOzgvTizJmCE3oqINuCdgw5aoJfi7FwNEwleQ87qhvHVz8UPY2lp9F3B2KSrQ_pJuiKVMXmhvxUbZnqyub_H2_kREQ3</addsrcrecordid><sourcetype>Open Access Repository</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype></control><display><type>article</type><title>Cooperation and Personalization on a Seesaw: Choice-based FL for Safe Cooperation in Wireless Networks</title><source>arXiv.org</source><creator>Zhang, Han ; Elsayed, Medhat ; Bavand, Majid ; Gaigalas, Raimundas ; Ozcan, Yigit ; Erol-Kantarci, Melike</creator><creatorcontrib>Zhang, Han ; Elsayed, Medhat ; Bavand, Majid ; Gaigalas, Raimundas ; Ozcan, Yigit ; Erol-Kantarci, Melike</creatorcontrib><description>Federated learning (FL) is an innovative distributed artificial intelligence
(AI) technique. It has been used for interdisciplinary studies in different
fields such as healthcare, marketing and finance. However the application of FL
in wireless networks is still in its infancy. In this work, we first overview
benefits and concerns when applying FL to wireless networks. Next, we provide a
new perspective on existing personalized FL frameworks by analyzing the
relationship between cooperation and personalization in these frameworks.
Additionally, we discuss the possibility of tuning the cooperation level with a
choice-based approach. Our choice-based FL approach is a flexible and safe FL
framework that allows participants to lower the level of cooperation when they
feel unsafe or unable to benefit from the cooperation. In this way, the
choice-based FL framework aims to address the safety and fairness concerns in
FL and protect participants from malicious attacks.</description><identifier>DOI: 10.48550/arxiv.2411.04159</identifier><language>eng</language><subject>Computer Science - Artificial Intelligence ; Computer Science - Distributed, Parallel, and Cluster Computing ; Computer Science - Learning ; Computer Science - Networking and Internet Architecture</subject><creationdate>2024-11</creationdate><rights>http://arxiv.org/licenses/nonexclusive-distrib/1.0</rights><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>228,230,776,881</link.rule.ids><linktorsrc>$$Uhttps://arxiv.org/abs/2411.04159$$EView_record_in_Cornell_University$$FView_record_in_$$GCornell_University$$Hfree_for_read</linktorsrc><backlink>$$Uhttps://doi.org/10.48550/arXiv.2411.04159$$DView paper in arXiv$$Hfree_for_read</backlink></links><search><creatorcontrib>Zhang, Han</creatorcontrib><creatorcontrib>Elsayed, Medhat</creatorcontrib><creatorcontrib>Bavand, Majid</creatorcontrib><creatorcontrib>Gaigalas, Raimundas</creatorcontrib><creatorcontrib>Ozcan, Yigit</creatorcontrib><creatorcontrib>Erol-Kantarci, Melike</creatorcontrib><title>Cooperation and Personalization on a Seesaw: Choice-based FL for Safe Cooperation in Wireless Networks</title><description>Federated learning (FL) is an innovative distributed artificial intelligence
(AI) technique. It has been used for interdisciplinary studies in different
fields such as healthcare, marketing and finance. However the application of FL
in wireless networks is still in its infancy. In this work, we first overview
benefits and concerns when applying FL to wireless networks. Next, we provide a
new perspective on existing personalized FL frameworks by analyzing the
relationship between cooperation and personalization in these frameworks.
Additionally, we discuss the possibility of tuning the cooperation level with a
choice-based approach. Our choice-based FL approach is a flexible and safe FL
framework that allows participants to lower the level of cooperation when they
feel unsafe or unable to benefit from the cooperation. In this way, the
choice-based FL framework aims to address the safety and fairness concerns in
FL and protect participants from malicious attacks.</description><subject>Computer Science - Artificial Intelligence</subject><subject>Computer Science - Distributed, Parallel, and Cluster Computing</subject><subject>Computer Science - Learning</subject><subject>Computer Science - Networking and Internet Architecture</subject><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2024</creationdate><recordtype>article</recordtype><sourceid>GOX</sourceid><recordid>eNqFjrEKwjAURbM4iPoBTr4faG20BXUtFgcRoYJjeNoXDNakvBSrfr20Org5XThcDkeIsYzCeJEk0RT5Ye7hLJYyjGKZLPtCp85VxFgbZwFtAXti7yyW5vVhLYacyGOzgvTizJmCE3oqINuCdgw5aoJfi7FwNEwleQ87qhvHVz8UPY2lp9F3B2KSrQ_pJuiKVMXmhvxUbZnqyub_H2_kREQ3</recordid><startdate>20241106</startdate><enddate>20241106</enddate><creator>Zhang, Han</creator><creator>Elsayed, Medhat</creator><creator>Bavand, Majid</creator><creator>Gaigalas, Raimundas</creator><creator>Ozcan, Yigit</creator><creator>Erol-Kantarci, Melike</creator><scope>AKY</scope><scope>GOX</scope></search><sort><creationdate>20241106</creationdate><title>Cooperation and Personalization on a Seesaw: Choice-based FL for Safe Cooperation in Wireless Networks</title><author>Zhang, Han ; Elsayed, Medhat ; Bavand, Majid ; Gaigalas, Raimundas ; Ozcan, Yigit ; Erol-Kantarci, Melike</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-arxiv_primary_2411_041593</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2024</creationdate><topic>Computer Science - Artificial Intelligence</topic><topic>Computer Science - Distributed, Parallel, and Cluster Computing</topic><topic>Computer Science - Learning</topic><topic>Computer Science - Networking and Internet Architecture</topic><toplevel>online_resources</toplevel><creatorcontrib>Zhang, Han</creatorcontrib><creatorcontrib>Elsayed, Medhat</creatorcontrib><creatorcontrib>Bavand, Majid</creatorcontrib><creatorcontrib>Gaigalas, Raimundas</creatorcontrib><creatorcontrib>Ozcan, Yigit</creatorcontrib><creatorcontrib>Erol-Kantarci, Melike</creatorcontrib><collection>arXiv Computer Science</collection><collection>arXiv.org</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Zhang, Han</au><au>Elsayed, Medhat</au><au>Bavand, Majid</au><au>Gaigalas, Raimundas</au><au>Ozcan, Yigit</au><au>Erol-Kantarci, Melike</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Cooperation and Personalization on a Seesaw: Choice-based FL for Safe Cooperation in Wireless Networks</atitle><date>2024-11-06</date><risdate>2024</risdate><abstract>Federated learning (FL) is an innovative distributed artificial intelligence
(AI) technique. It has been used for interdisciplinary studies in different
fields such as healthcare, marketing and finance. However the application of FL
in wireless networks is still in its infancy. In this work, we first overview
benefits and concerns when applying FL to wireless networks. Next, we provide a
new perspective on existing personalized FL frameworks by analyzing the
relationship between cooperation and personalization in these frameworks.
Additionally, we discuss the possibility of tuning the cooperation level with a
choice-based approach. Our choice-based FL approach is a flexible and safe FL
framework that allows participants to lower the level of cooperation when they
feel unsafe or unable to benefit from the cooperation. In this way, the
choice-based FL framework aims to address the safety and fairness concerns in
FL and protect participants from malicious attacks.</abstract><doi>10.48550/arxiv.2411.04159</doi><oa>free_for_read</oa></addata></record> |
| fulltext | fulltext_linktorsrc |
| identifier | DOI: 10.48550/arxiv.2411.04159 |
| ispartof | |
| issn | |
| language | eng |
| recordid | cdi_arxiv_primary_2411_04159 |
| source | arXiv.org |
| subjects | Computer Science - Artificial Intelligence Computer Science - Distributed, Parallel, and Cluster Computing Computer Science - Learning Computer Science - Networking and Internet Architecture |
| title | Cooperation and Personalization on a Seesaw: Choice-based FL for Safe Cooperation in Wireless Networks |
| url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-23T20%3A55%3A38IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-arxiv_GOX&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Cooperation%20and%20Personalization%20on%20a%20Seesaw:%20Choice-based%20FL%20for%20Safe%20Cooperation%20in%20Wireless%20Networks&rft.au=Zhang,%20Han&rft.date=2024-11-06&rft_id=info:doi/10.48550/arxiv.2411.04159&rft_dat=%3Carxiv_GOX%3E2411_04159%3C/arxiv_GOX%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_id=info:pmid/&rfr_iscdi=true |