Neuroimage data collected from multiple research institutions may incur additional source dependency, affecting the overall statistical power and leading to erroneous conclusions. This problem can be mitigated with data harmonization approaches. While open neuroimaging datasets are becoming more common, a substantial amount of data can still not be shared for various reasons. In addition, current approaches require moving all the data to a central location, which requires additional resources and creates redundant copies of the same datasets. To address these issues, we propose a decentralized harmonization approach called "Decentralized ComBat" that performs remote operations on the datasets separately without sharing individual subject data, ensuring a certain level of privacy and reducing regulatory hurdles. The study was conducted on harmonizing functional connectivity. Results showed similar performance as the centralized ComBat algorithm in a decentralized environment.