Summary of Federated Instruction Tuning Of Llms with Domain Coverage Augmentation, by Zezhou Wang et al.
Federated Instruction Tuning of LLMs with Domain Coverage Augmentation
by Zezhou Wang, Yaxin Du, Xingjun Ma, Yugang Jiang, Zhuzhong Qian, Siheng Chen
First submitted to arxiv on: 30 Sep 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computation and Language (cs.CL); Distributed, Parallel, and Cluster Computing (cs.DC)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary Federated Domain-specific Instruction Tuning (FedDIT) is a machine learning approach that leverages limited private data from multiple clients to improve model performance within specific domains. By combining instruction augmentation strategies with cross-client private data, FedDIT enhances model accuracy in targeted domains. Our research reveals that the key factor driving FedDIT’s success lies not in data heterogeneity but rather in domain coverage across clients. To address this, we propose FedDCA, a method that optimizes domain coverage through greedy client center selection and retrieval-based augmentation. We also introduce FedDCA, avariantofFedDCAthatutilizesheterogeneousencoderswithserver − sidefeaturealignmentforcomputationalefficiencyandsystemscalability.Ourexperimentsdemonstratetheeffectivenessofbothmethodsacrossvariousdomains, includingcode, medical, financial, andmathematicaltasks.Moreover, weanalyzeprivacypreservationagainstmemoryextractionattacks, showingthatwhilesomeriskremains, itdecreasesastrainingprogresses. < /td > < /tr > < tr > < td > Low < /td > < td > GrooveSquid.com(originalcontent) < /td > < td > < strong > LowDifficultySummary < /strong > < br > Imagineyouhavealotofdifferentdatasources, likecomputersorhospitals, eachwithitsowninformation.FederatedDomain − specificInstructionTuning(FedDIT)isawaytousethisdatatoimprovehowwellmachinelearningmodelsworkinspecificareas.WefoundthatthekeytomakingFedDITsuccessfulliesnotinhowmuchvarietythereisinthedatabutratherinhowwellwecoverdifferentdomainsacrossallthesources.Tomakethisprocessmoreefficient, wedevelopedtwonewmethods : FedDCAanditsvariant, FedDCA. These methods can work with a lot of different types of data and are very effective at improving model performance in various areas like code, medicine, finance, and math. We also looked into how well these methods protect privacy and found that while there is some risk involved, it decreases as the models train more. |
Keywords
» Artificial intelligence » Alignment » Instruction tuning » Machine learning
