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How can we adapt acoustic scene classification (ASC) models to an unlabeled target domain in the absence of original source data? This challenge is critical in real-world ASC, where device heterogeneity (e.g., varying microphone frequency responses) severely degrades adaptation performance. While Multi-Source-Free Domain Adaptation (MSFDA) offers a privacy-compliant solution, effective aggregation is hindered by inconsistent prediction biases and unknown acoustic similarities between source and target devices. We propose FᴀSᴏLᴀ, a robust MSFDA framework designed to mitigate device heterogeneity by integrating posterior adjustment to correct bias through alignment with the estimated target priors, and label agreement to prioritize reliable models based on prediction consistency. Experimental results indicate that FᴀSᴏLᴀ effectively handles multi-domain shifts and significantly improves adaptation performance.