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Large Language Models
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+have+revolutionized+natural+language+processing+tasks.+However%252C+their+deployment+in+wireless+networks+still+face+challenges%252C+i.e.%252C+a+lack+of+privacy+and+security+protection+mechanisms.+Federated+Learning+(FL)+has+emerged+as+a+promising+approach+to+address+these+challenges.+Yet%252C+it+suffers+from+issues+including+inefficient+handling+with+big+and+heterogeneous+data%252C+resource-intensive+training%252C+and+high+communication+overhead.+To+tackle+these+issues%252C+we+first+compare+different+learning+stages+and+their+features+of+LLMs+in+wireless+networks.+Next%252C+we+introduce+two+personalized+wireless+federated+fine-tuning+methods+with+low+communication+overhead%252C+i.e.%252C+(1)+Personalized+Federated+Instruction+Tuning+(PFIT)%252C+which+employs+reinforcement+learning+to+fine-tune+local+LLMs+with+diverse+reward+models+to+achieve+personalization%253B+(2)+Personalized+Federated+Task+Tuning+(PFTT)%252C+which+can+leverage+global+adapters+and+local+Low-Rank+Adaptations+(LoRA)+to+collaboratively+fine-tune+local+LLMs%252C+where+the+local+LoRAs+can+be+applied+to+achieve+personalization+without+aggregation.+Finally%252C+we+perform+simulations+to+demonstrate+the+effectiveness+of+the+proposed+two+methods+and+comprehensively+discuss+open+issues.&cdnurl=https%3a%2f%2fth.bing.com%2fth%2fid%2fR.5c17a6388c0b51a7d57e89aec3ae0530%3frik%3dAig5yKNtD5BIxg%26pid%3dImgRaw%26r%3d0&exph=630&expw=1200&q=Large+Language+Models+Attention+Embedded+and+Feedforward+Layers&simid=607989025968111125&FORM=IRPRST&ck=CA2C546E41D66E7E55260D6A7DCE9B04&selectedIndex=18&itb=0)
